Relevant bibliographies by topics / Invariant representation learning

Academic literature on the topic 'Invariant representation learning'

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Published: 10 December 2022
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Journal articles on the topic "Invariant representation learning"

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Zhu, Zheng-Mao, Shengyi Jiang, Yu-Ren Liu, Yang Yu, and Kun Zhang. "Invariant Action Effect Model for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 9260–68. http://dx.doi.org/10.1609/aaai.v36i8.20913.

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Good representations can help RL agents perform concise modeling of their surroundings, and thus support effective decision-making in complex environments. Previous methods learn good representations by imposing extra constraints on dynamics. However, in the causal perspective, the causation between the action and its effect is not fully considered in those methods, which leads to the ignorance of the underlying relations among the action effects on the transitions. Based on the intuition that the same action always causes similar effects among different states, we induce such causation by taking the invariance of action effects among states as the relation. By explicitly utilizing such invariance, in this paper, we show that a better representation can be learned and potentially improves the sample efficiency and the generalization ability of the learned policy. We propose Invariant Action Effect Model (IAEM) to capture the invariance in action effects, where the effect of an action is represented as the residual of representations from neighboring states. IAEM is composed of two parts: (1) a new contrastive-based loss to capture the underlying invariance of action effects; (2) an individual action effect and provides a self-adapted weighting strategy to tackle the corner cases where the invariance does not hold. The extensive experiments on two benchmarks, i.e. Grid-World and Atari, show that the representations learned by IAEM preserve the invariance of action effects. Moreover, with the invariant action effect, IAEM can accelerate the learning process by 1.6x, rapidly generalize to new environments by fine-tuning on a few components, and outperform other dynamics-based representation methods by 1.4x in limited steps.
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Shui, Changjian, Boyu Wang, and Christian Gagné. "On the benefits of representation regularization in invariance based domain generalization." Machine Learning 111, no. 3 (January 1, 2022): 895–915. http://dx.doi.org/10.1007/s10994-021-06080-w.

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AbstractA crucial aspect of reliable machine learning is to design a deployable system for generalizing new related but unobserved environments. Domain generalization aims to alleviate such a prediction gap between the observed and unseen environments. Previous approaches commonly incorporated learning the invariant representation for achieving good empirical performance. In this paper, we reveal that merely learning the invariant representation is vulnerable to the related unseen environment. To this end, we derive a novel theoretical analysis to control the unseen test environment error in the representation learning, which highlights the importance of controlling the smoothness of representation. In practice, our analysis further inspires an efficient regularization method to improve the robustness in domain generalization. The proposed regularization is orthogonal to and can be straightforwardly adopted in existing domain generalization algorithms that ensure invariant representation learning. Empirical results show that our algorithm outperforms the base versions in various datasets and invariance criteria.
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Hyun, Jaeguk, ChanYong Lee, Hoseong Kim, Hyunjung Yoo, and Eunjin Koh. "Learning Domain Invariant Representation via Self-Rugularization." Journal of the Korea Institute of Military Science and Technology 24, no. 4 (August 5, 2021): 382–91. http://dx.doi.org/10.9766/kimst.2021.24.4.382.

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Unsupervised domain adaptation often gives impressive solutions to handle domain shift of data. Most of current approaches assume that unlabeled target data to train is abundant. This assumption is not always true in practices. To tackle this issue, we propose a general solution to solve the domain gap minimization problem without any target data. Our method consists of two regularization steps. The first step is a pixel regularization by arbitrary style transfer. Recently, some methods bring style transfer algorithms to domain adaptation and domain generalization process. They use style transfer algorithms to remove texture bias in source domain data. We also use style transfer algorithms for removing texture bias, but our method depends on neither domain adaptation nor domain generalization paradigm. The second regularization step is a feature regularization by feature alignment. Adding a feature alignment loss term to the model loss, the model learns domain invariant representation more efficiently. We evaluate our regularization methods from several experiments both on small dataset and large dataset. From the experiments, we show that our model can learn domain invariant representation as much as unsupervised domain adaptation methods.
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Aggarwal, Karan, Shafiq Joty, Luis Fernandez-Luque, and Jaideep Srivastava. "Adversarial Unsupervised Representation Learning for Activity Time-Series." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 834–41. http://dx.doi.org/10.1609/aaai.v33i01.3301834.

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Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health. The rapid increase in the popularity of wearable devices pro-vides a significant new source, making it possible to track the user’s lifestyle real-time. In this paper, we propose a novel unsupervised representation learning technique called activ-ity2vecthat learns and “summarizes” the discrete-valued ac-tivity time-series. It learns the representations with three com-ponents: (i) the co-occurrence and magnitude of the activ-ity levels in a time-segment, (ii) neighboring context of the time-segment, and (iii) promoting subject-invariance with ad-versarial training. We evaluate our method on four disorder prediction tasks using linear classifiers. Empirical evaluation demonstrates that our proposed method scales and performs better than many strong baselines. The adversarial regime helps improve the generalizability of our representations by promoting subject invariant features. We also show that using the representations at the level of a day works the best since human activity is structured in terms of daily routines.
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Wu, Yue, Hongfu Liu, Jun Li, and Yun Fu. "Improving face representation learning with center invariant loss." Image and Vision Computing 79 (November 2018): 123–32. http://dx.doi.org/10.1016/j.imavis.2018.09.010.

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Byrne, Patrick, and Suzanna Becker. "A Principle for Learning Egocentric-Allocentric Transformation." Neural Computation 20, no. 3 (March 2008): 709–37. http://dx.doi.org/10.1162/neco.2007.10-06-361.

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Numerous single-unit recording studies have found mammalian hippocampal neurons that fire selectively for the animal's location in space, independent of its orientation. The population of such neurons, commonly known as place cells, is thought to maintain an allocentric, or orientation-independent, internal representation of the animal's location in space, as well as mediating long-term storage of spatial memories. The fact that spatial information from the environment must reach the brain via sensory receptors in an inherently egocentric, or viewpoint-dependent, fashion leads to the question of how the brain learns to transform egocentric sensory representations into allocentric ones for long-term memory storage. Additionally, if these long-term memory representations of space are to be useful in guiding motor behavior, then the reverse transformation, from allocentric to egocentric coordinates, must also be learned. We propose that orientation-invariant representations can be learned by neural circuits that follow two learning principles: minimization of reconstruction error and maximization of representational temporal inertia. Two different neural network models are presented that adhere to these learning principles, the first by direct optimization through gradient descent and the second using a more biologically realistic circuit based on the restricted Boltzmann machine (Hinton, 2002; Smolensky, 1986). Both models lead to orientation-invariant representations, with the latter demonstrating place-cell-like responses when trained on a linear track environment.
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Xu, Qi, Liang Yao, Zhengkai Jiang, Guannan Jiang, Wenqing Chu, Wenhui Han, Wei Zhang, Chengjie Wang, and Ying Tai. "DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2884–92. http://dx.doi.org/10.1609/aaai.v36i3.20193.

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Model generalization to the unseen scenes is crucial to real-world applications, such as autonomous driving, which requires robust vision systems. To enhance the model generalization, domain generalization through learning the domain-invariant representation has been widely studied. However, most existing works learn the shared feature space within multi-source domains but ignore the characteristic of the feature itself (e.g., the feature sensitivity to the domain-specific style). Therefore, we propose the Domain-invariant Representation Learning (DIRL) for domain generalization which utilizes the feature sensitivity as the feature prior to guide the enhancement of the model generalization capability. The guidance reflects in two folds: 1) Feature re-calibration that introduces the Prior Guided Attention Module (PGAM) to emphasize the insensitive features and suppress the sensitive features. 2): Feature whiting that proposes the Guided Feature Whiting (GFW) to remove the feature correlations which are sensitive to the domain-specific style. We construct the domain-invariant representation which suppresses the effect of the domain-specific style on the quality and correlation of the features. As a result, our method is simple yet effective, and can enhance the robustness of various backbone networks with little computational cost. Extensive experiments over multiple domains generalizable segmentation tasks show the superiority of our approach to other methods.
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Qin, Cao, Yunzhou Zhang, Yan Liu, Sonya Coleman, Dermot Kerr, and Guanghao Lv. "Appearance-invariant place recognition by adversarially learning disentangled representation." Robotics and Autonomous Systems 131 (September 2020): 103561. http://dx.doi.org/10.1016/j.robot.2020.103561.

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Liang, Sen, Zhi-ze Zhou, Yu-dong Guo, Xuan Gao, Ju-yong Zhang, and Hu-jun Bao. "Facial landmark disentangled network with variational autoencoder." Applied Mathematics-A Journal of Chinese Universities 37, no. 2 (June 2022): 290–305. http://dx.doi.org/10.1007/s11766-022-4589-0.

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AbstractLearning disentangled representation of data is a key problem in deep learning. Specifically, disentangling 2D facial landmarks into different factors (e.g., identity and expression) is widely used in the applications of face reconstruction, face reenactment and talking head et al.. However, due to the sparsity of landmarks and the lack of accurate labels for the factors, it is hard to learn the disentangled representation of landmarks. To address these problem, we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations, which is based on a Variational Autoencoder framework. Besides, we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage. Moreover, we implement an identity preservation loss to further enhance the representation ability of identity factor. To the best of our knowledge, this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.
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Bradski, Gary, Gail A. Carpenter, and Stephen Grossberg. "Working Memory Networks for Learning Temporal Order with Application to Three-Dimensional Visual Object Recognition." Neural Computation 4, no. 2 (March 1992): 270–86. http://dx.doi.org/10.1162/neco.1992.4.2.270.

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Working memory neural networks, called Sustained Temporal Order REcurrent (STORE) models, encode the invariant temporal order of sequential events in short-term memory (STM). Inputs to the networks may be presented with widely differing growth rates, amplitudes, durations, and interstimulus intervals without altering the stored STM representation. The STORE temporal order code is designed to enable groupings of the stored events to be stably learned and remembered in real time, even as new events perturb the system. Such invariance and stability properties are needed in neural architectures which self-organize learned codes for variable-rate speech perception, sensorimotor planning, or three-dimensional (3-D) visual object recognition. Using such a working memory, a self-organizing architecture for invariant 3-D visual object recognition is described. The new model is based on the model of Seibert and Waxman (1990a), which builds a 3-D representation of an object from a temporally ordered sequence of its two-dimensional (2-D) aspect graphs. The new model, called an ARTSTORE model, consists of the following cascade of processing modules: Invariant Preprocessor → ART 2 → STORE Model → ART 2 → Outstar Network.
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Dissertations / Theses on the topic "Invariant representation learning"

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Li, Nuo Ph D. Massachusetts Institute of Technology. "Unsupervised learning of invariant object representation in primate visual cortex." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65288.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Visual object recognition (categorization and identification) is one of the most fundamental cognitive functions for our survival. Our visual system has the remarkable ability to convey to us visual object and category information in a manner that is largely tolerant ("invariant") to the exact position, size, pose of the object, illumination, and clutter. The ventral visual stream in non-human primate has solved this problem. At the highest stage of the visual hierarchy, the inferior temporal cortex (IT), neurons have selectivity for objects and maintain that selectivity across variations in the images. A reasonably sized population of these tolerant neurons can support object recognition. However, we do not yet understand how IT neurons construct this neuronal tolerance. The aim of this thesis is to tackle this question and to examine the hypothesis that the ventral visual stream may leverage experience to build its neuronal tolerance. One potentially powerful idea is that time can act as an implicit teacher, in that each object's identity tends to remain temporally stable, thus different retinal images of the same object are temporally contiguous. In theory, the ventral stream could take advantage of this natural tendency and learn to associate together the neuronal representations of temporally contiguous retinal images to yield tolerant object selectivity in IT cortex. In this thesis, I report neuronal support for this hypothesis in IT of non-human primates. First, targeted alteration of temporally contiguous experience with object images at different retinal positions rapidly reshaped IT neurons' position tolerance. Second, similar temporal contiguity manipulation of experience with object images at different sizes similarly reshaped IT size tolerance. These instances of experience-induced effect were similar in magnitude, grew gradually stronger with increasing visual experience, and the size of the effect was large. Taken together, these studies show that unsupervised, temporally contiguous experience can reshape and build at least two types of IT tolerance, and that they can do so under a wide range of spatiotemporal regimes encountered during natural visual exploration. These results suggest that the ventral visual stream uses temporal contiguity visual experience with a general unsupervised tolerance learning (UTL) mechanism to build its invariant object representation.
by Nuo Li.
Ph.D.
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Lu, Danni. "Representation Learning Based Causal Inference in Observational Studies." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/102426.

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This dissertation investigates novel statistical approaches for causal effect estimation in observational settings, where controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) to restore the covariate balance between treatment groups and (ii) to attenuate spurious correlations in training data to derive valid causal conclusions that generalize. By incorporating ideas from representation learning, adversarial matching, generative causal estimation, and invariant risk modeling, this dissertation establishes a causal framework that balances the covariate distribution in latent representation space to yield individualized estimations, and further contributes novel perspectives on causal effect estimation based on invariance principles. The dissertation begins with a systematic review and examination of classical propensity score based balancing schemes for population-level causal effect estimation, presented in Chapter 2. Three causal estimands that target different foci in the population are considered: average treatment effect on the whole population (ATE), average treatment effect on the treated population (ATT), and average treatment effect on the overlap population (ATO). The procedure is demonstrated in a naturalistic driving study (NDS) to evaluate the causal effect of cellphone distraction on crash risk. While highlighting the importance of adopting causal perspectives in analyzing risk factors, discussions on the limitations in balance efficiency, robustness against high-dimensional data and complex interactions, and the need for individualization are provided to motivate subsequent developments. Chapter 3 presents a novel generative Bayesian causal estimation framework named Balancing Variational Neural Inference of Causal Effects (BV-NICE). Via appealing to the Robinson factorization and a latent Bayesian model, a novel variational bound on likelihood is derived, explicitly characterized by the causal effect and propensity score. Notably, by treating observed variables as noisy proxies of unmeasurable latent confounders, the variational posterior approximation is re-purposed as a stochastic feature encoder that fully acknowledges representation uncertainties. To resolve the imbalance in representations, BV-NICE enforces KL-regularization on the respective representation marginals using Fenchel mini-max learning, justified by a new generalization bound on the counterfactual prediction accuracy. The robustness and effectiveness of this framework are demonstrated through an extensive set of tests against competing solutions on semi-synthetic and real-world datasets. In recognition of the reliability issue when extending causal conclusions beyond training distributions, Chapter 4 argues ascertaining causal stability is the key and introduces a novel procedure called Risk Invariant Causal Estimation (RICE). By carefully re-examining the relationship between statistical invariance and causality, RICE cleverly leverages the observed data disparities to enable the identification of stable causal effects. Concretely, the causal inference objective is reformulated under the framework of invariant risk modeling (IRM), where a population-optimality penalty is enforced to filter out un-generalizable effects across heterogeneous populations. Importantly, RICE allows settings where counterfactual reasoning with unobserved confounding or biased sampling designs become feasible. The effectiveness of this new proposal is verified with respect to a variety of study designs on real and synthetic data. In summary, this dissertation presents a flexible causal inference framework that acknowledges the representation uncertainties and data heterogeneities. It enjoys three merits: improved balance to complex covariate interactions, enhanced robustness to unobservable latent confounders, and better generalizability to novel populations.
Doctor of Philosophy
Reasoning cause and effect is the innate ability of a human. While the drive to understand cause and effect is instinct, the rigorous reasoning process is usually trained through the observation of countless trials and failures. In this dissertation, we embark on a journey to explore various principles and novel statistical approaches for causal inference in observational studies. Throughout the dissertation, we focus on the causal effect estimation which answers questions like ``what if" and ``what could have happened". The causal effect of a treatment is measured by comparing the outcomes corresponding to different treatment levels of the same unit, e.g. ``what if the unit is treated instead of not treated?". The challenge lies in the fact that i) a unit only receives one treatment at a time and therefore it is impossible to directly compare outcomes of different treatment levels; ii) comparing the outcomes across different units may involve bias due to confounding as the treatment assignment potentially follows a systematic mechanism. Therefore, deconfounding constructs the main hurdle in estimating causal effects. This dissertation presents two parallel principles of deconfounding: i) balancing, i.e., comparing difference under similar conditions; ii) contrasting, i.e., extracting invariance under heterogeneous conditions. Chapter 2 and Chapter 3 explore causal effect through balancing, with the former systematically reviews a classical propensity score weighting approach in a conventional data setting and the latter presents a novel generative Bayesian framework named Balancing Variational Neural Inference of Causal Effects(BV-NICE) for high-dimensional, complex, and noisy observational data. It incorporates the advance deep learning techniques of representation learning, adversarial learning, and variational inference. The robustness and effectiveness of the proposed framework are demonstrated through an extensive set of experiments. Chapter 4 extracts causal effect through contrasting, emphasizing that ascertaining stability is the key of causality. A novel causal effect estimating procedure called Risk Invariant Causal Estimation(RICE) is proposed that leverages the observed data disparities to enable the identification of stable causal effects. The improved generalizability of RICE is demonstrated through synthetic data with different structures, compared with state-of-art models. In summary, this dissertation presents a flexible causal inference framework that acknowledges the data uncertainties and heterogeneities. By promoting two different aspects of causal principles and integrating advance deep learning techniques, the proposed framework shows improved balance for complex covariate interactions, enhanced robustness for unobservable latent confounders, and better generalizability for novel populations.
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Woodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.

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Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of reconstructing proper networks, and this objective will be addressed in three parts. The first part lays the foundation for the theory of mathematical representations of proper networks, including an exposition on when such networks are well-posed (i.e., physically realizable). The second part studies the notions of abstractions of a network, which are other networks that preserve certain properties of the original network but contain less structural information. As such, abstractions require less a priori information to reconstruct from data than the original network, which allows previously-unsolvable problems to become solvable. The third part addresses our original objective and presents reconstruction algorithms to recover proper networks in both the time domain and in the frequency domain.
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Dupuy, Eric. "Construction d’une notion scientifique et invariant : le cas d'élèves de l'enseignement primaire." Thesis, Bordeaux 2, 2009. http://www.theses.fr/2009BOR21652/document.

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Cette thèse centre son objet dans le champ de la construction de conceptions scientifiques au cours d’activités expérimentales en sciences physiques menées en milieu scolaire par des élèves. Elle s’appuie sur trois hypothèses majeures. La formation des concepts et des notions se structure autour d’éléments invariants. L’élaboration de la pensée résulte de la conjonction de réflexions propres, d’actions et d’échanges ancrés dans une dynamique sociale. Les représentations dévoilent et organisent des modalités de pensée et de son actualisation. Dans une premier temps la thèse se concentre sur la formation de la notion de concept : du repérage d’invariants vers une architecture conceptuelle stable. Ensuite elle expose les questions que posent la notion d’apprentissage et la perspective de l’autonomie de l’apprenant. Enfin elle présente la théorie de la représentation et pose la question de la constitution et de la mise en évidence de la connaissance. Dans un second temps, la thèse inscrit ses expérimentations dans l’observation de situations scolaires basant sur l’hypothèse phénoménologique dans une épistémologie constructiviste la condition de la transformation des situations vécues en données exploitables. L’une sur le thème de l’ombre, l’autre sur celui de l’électricité, elles attestent d’une élaboration cognitive complexe d’où naissent des conceptions sur la base d’invariants, les représentations permettent de dévoiler et de structurer des cheminements de pensée. Si les items “enfantins” (R1) sont nombreux, des items “rationalisants” (de R2) se dégagent souvent portés par une représentation “imagée” (R1?R2), ou sur une dynamique interne (R2?R2). Enfin, la thèse montre, de manière encore empirique, comment certaines combinaisons d’items manifestent, pour ainsi dire “sous nos yeux”, la pensée de l’élève qui s’élabore : une “enaction” au sens de Varela
The purpose of this dissertation is to study how scientific conceptions are constructed in the course of experimental activities in physical sciences by young children at school. The study is based on three principal hypotheses: a) The formation of concepts and notions depends on invariant elements. b) The elaboration of thought results from personal reflections, actions and exchanges all anchored in a social dynamic process. c) Representations reveal and organise modes of thinking and their actualisation. In the first stage, the dissertation focuses on the formation of the notion of concept: from the evidencing of invariants to a stable conceptual architecture. Next, it presents the questions raised by the notion of learning and the expected achievement of the learner’s autonomy. Then, it develops a theory of representation, considering the question of the constitution and realisation of knowledge. In a second stage, the dissertation conducts its experimentations within the framework of an observation of classroom situations, the conversion of concrete situations into interpretable data being based on the phenomenological hypothesis from the point of view of constructivist epistemology. One situation refers to the theme of shade, the other to that of electricity: both evidence a complex process of cognitive elaboration, giving rise to conceptions based on a set of invariants. The representations thus reveal and structure the processes of thought. While « childish » items (R1) prove to be numerous, there also often emerge « rationalising » items (R2), either image-based or resting on internal dynamics. Finally, the dissertation demonstrates, in a still empirical way, how certain item combinations evince, so to speak before our very eyes, the child’s process of thinking in action — i.e. « enaction » in the Varela sense of the word
Diese Arbeit befasst sich mit der Konstruktion von wissenschaftlichen Konzepten im Verlauf von physikalischen Experimenten ,die Schüler im Unterricht durchführen. Sie stützt sich dabei auf drei Hypothesen. Die Bildung von Konzepten und Begriffen strukturiert sich um Invarianten. Die Erarbeitung eines Gedankens ergibt sich aus der Verbindung von eigenständigen Überlegungen, von Handlungen und von in sozialer Dynamik verankertem Austausch. Repräsentationen zeigen Modalitäten des Denkens und ihre Aktualisierung auf und organisieren sie. Diese Arbeit fokalisiert sich zunächst auf die Ausbildung des Konzeptbegriffs: vom Erfassen von Invarianten hin zu einer stabilen Konzeptarchitektur. Dann geht sie auf die Fragestellungen des Lernbegriffs ein und auf die Perspektive der Autonomie des Lernenden. Schließlich stellt sie die Repräsentationstheorie dar und fragt nach der Ausformung und der Offenkundigkeit der Erkenntnis. Im zweiten Teil dieser Arbeit werden die Experimente in Form von Beobachtungen in der Schule ausgewertet. Dabei beruht die Umwandlung von erlebten Situationen in verwertbare Daten auf der phänomenologischen Hypothese einer konstruktivistischen Epistemologie. Ein Experiment beschäftigt sich mit dem Schatten, das andere mit dem Thema Elektrizität. Sie belegen eine komplexe kognitive Erarbeitung, die zu Konzepten auf der Grundlage von Invarianten führen. Durch Repräsentationen werden die Gedankengänge offensichtlich und strukturiert. Auch wenn es zahlreich „kindliche“ Item (R1) gibt, werden „rationalisierende“ Item (von R2) oft mit Hilfe einer „bildgebenden“ Repräsentation (R1?R2) oder einer internen Dynamik (R2?R2) freigesetzt. Auf noch empirische Weise zeigt diese Arbeit schließlich wie gewisse Kombinationen von Item sozusagen unter unseren Augen die Entstehung des Gedanken beim Schüler aufzeigen: eine Enaction im Sinne von Varela
Esta tesis centra su objeto en el campo de la construcción de concepciones científicas en el curso de actividades experimentales en ciencias físicas conducidas en medio escolar por alumnos. Se apoya sobre tres hipótesis mayores. La formación de los conceptos y de nociones se estructura alrededor de elementos invariantes. La elaboración del pensamiento resulta de la conjunción de reflexiones, propias acciones e intercambios anclados en una dinámica social. Las representaciones descubren y organizan las modalidades de pensamiento y su actualización. En un primer tiempo la tesis se concentra en la formación de la noción de concepto: del reconocimiento de invariantes hacia una arquitectura conceptual estable. Luego expone las preguntas que plantea la noción de aprendizaje y la perspectiva de autonomía del novato. Luego presenta la teoría de la representación y plantea la cuestión de la constitución y la puesta en evidencia del conocimiento. En un segundo tiempo, la tesis inscribe sus experimentaciones en la observación de situaciones escolares basada la hipótesis fenomenológica en una epistemológica constructivista, la condición de la transformación de situaciones vividas en datos explotables. Una sobre el tema de la sombra, otra en lo de la electricidad, dan testimonio de una elaboración cognitiva compleja de donde nacen concepciones sobre la base de invariantes, las representaciones permiten descubrir y estructurar aproches de pensamiento. Si los “ítem” infantiles (R1) son numerosos, unos “ítem” “ racionalizantes (de R2) se desprenden a menudo llevados por una representación llena de imágenes (R1?R2), o en una dinámica interne (R2?R2). Por fin, la tesis muestra, de manera aun empÍrica, cómo ciertas combinaciones de ítem manifiestan, dicho sea asÍ “bajo nuestros ojos”, el pensamiento del alumno elaborándose: una “enacción” en el sentido de Varela
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Tacchetti, Andrea. "Learning invariant representations of actions and faces." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113935.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 125-139).
Recognizing other people and their actions from visual input is a crucial aspect of human perception that allows individuals to respond to social cues. Humans effortlessly identify familiar faces and are able to make fine distinctions between others' behaviors, despite transformations, like changes in viewpoint, lighting or facial expression, that substantially alter the appearance of a visual scene. The ability to generalize across these complex transformations is a hallmark of human visual intelligence, and the neural mechanisms supporting it have been the subject of wide ranging investigation in systems and computational neuroscience. However, advances in understanding the neural machinery of visual perception have not always translated in precise accounts of the computational principles dictating which representations of sensory input the human visual system learned to compute; nor how our visual system acquires the information necessary to support this learning process. Here we present results in support of the hypothesis that invariant discrimination and time continuity might fill these gaps. In particular, we use Magnetoencephalography decoding and a dataset of well-controlled, naturalistic videos to study invariant action recognition and find that representations of action sequences that support invariant recognition can be measured in the human brain. Moreover, we establish a direct link between how well artificial video representations support invariant action recognition and the extent to which they match neural correlation patterns. Finally, we show that visual representations of visual input that are robust to changes in appearance, can be learned by exploiting time continuity in video sequences. Taken as a whole our results suggest that supporting invariant discrimination tasks is the computational principle dictating which representations of sensory input are computed by human visual cortex and that time continuity in visual scenes is sufficient to learn such representations.
by Andrea Tacchetti.
Ph. D.
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Vedaldi, Andrea. "Invariant representations and learning for computer vision." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1676977531&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Evans, Benjamin D. "Learning transformation-invariant visual representations in spiking neural networks." Thesis, University of Oxford, 2012. https://ora.ox.ac.uk/objects/uuid:15bdf771-de28-400e-a1a7-82228c7f01e4.

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This thesis aims to understand the learning mechanisms which underpin the process of visual object recognition in the primate ventral visual system. The computational crux of this problem lies in the ability to retain specificity to recognize particular objects or faces, while exhibiting generality across natural variations and distortions in the view (DiCarlo et al., 2012). In particular, the work presented is focussed on gaining insight into the processes through which transformation-invariant visual representations may develop in the primate ventral visual system. The primary motivation for this work is the belief that some of the fundamental mechanisms employed in the primate visual system may only be captured through modelling the individual action potentials of neurons and therefore, existing rate-coded models of this process constitute an inadequate level of description to fully understand the learning processes of visual object recognition. To this end, spiking neural network models are formulated and applied to the problem of learning transformation-invariant visual representations, using a spike-time dependent learning rule to adjust the synaptic efficacies between the neurons. The ways in which the existing rate-coded CT (Stringer et al., 2006) and Trace (Földiák, 1991) learning mechanisms may operate in a simple spiking neural network model are explored, and these findings are then applied to a more accurate model using realistic 3-D stimuli. Three mechanisms are then examined, through which a spiking neural network may solve the problem of learning separate transformation-invariant representations in scenes composed of multiple stimuli by temporally segmenting competing input representations. The spike-time dependent plasticity in the feed-forward connections is then shown to be able to exploit these input layer dynamics to form individual stimulus representations in the output layer. Finally, the work is evaluated and future directions of investigation are proposed.
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Morère, Olivier André Luc. "Deep learning compact and invariant image representations for instance retrieval." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066406.

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Nous avons précédemment mené une étude comparative entre les descripteurs FV et CNN dans le cadre de la recherche par similarité d’instance. Cette étude montre notamment que les descripteurs issus de CNN manquent d’invariance aux transformations comme les rotations ou changements d’échelle. Nous montrons dans un premier temps comment des réductions de dimension (“pooling”) appliquées sur la base de données d’images permettent de réduire fortement l’impact de ces problèmes. Certaines variantes préservent la dimensionnalité des descripteurs associés à une image, alors que d’autres l’augmentent, au prix du temps d’exécution des requêtes. Dans un second temps, nous proposons la réduction de dimension emboitée pour l’invariance (NIP), une méthode originale pour la production, à partir de descripteurs issus de CNN, de descripteurs globaux invariants à de multiples transformations. La méthode NIP est inspirée de la théorie pour l’invariance “i-theory”, une théorie mathématique proposée il y a peu pour le calcul de transformations invariantes à des groupes au sein de réseaux de neurones acycliques. Nous montrons que NIP permet d’obtenir des descripteurs globaux compacts (mais non binaires) et robustes aux rotations et aux changements d’échelle, que NIP est plus performants que les autres méthodes à dimensionnalité équivalente sur la plupart des bases de données d’images. Enfin, nous montrons que la combinaison de NIP avec la méthode de hachage RBMH proposée précédemment permet de produire des codes binaires à la fois compacts et invariants à plusieurs types de transformations. La méthode NIP+RBMH, évaluée sur des bases de données d’images de moyennes et grandes échelles, se révèle plus performante que l’état de l’art, en particulier dans le cas de descripteurs binaires de très petite taille (de 32 à 256 bits)
Image instance retrieval is the problem of finding an object instance present in a query image from a database of images. Also referred to as particular object retrieval, this problem typically entails determining with high precision whether the retrieved image contains the same object as the query image. Scale, rotation and orientation changes between query and database objects and background clutter pose significant challenges for this problem. State-of-the-art image instance retrieval pipelines consist of two major steps: first, a subset of images similar to the query are retrieved from the database, and second, Geometric Consistency Checks (GCC) are applied to select the relevant images from the subset with high precision. The first step is based on comparison of global image descriptors: high-dimensional vectors with up to tens of thousands of dimensions rep- resenting the image data. The second step is computationally highly complex and can only be applied to hundreds or thousands of images in practical applications. More discriminative global descriptors result in relevant images being more highly ranked, resulting in fewer images that need to be compared pairwise with GCC. As a result, better global descriptors are key to improving retrieval performance and have been the object of much recent interest. Furthermore, fast searches in large databases of millions or even billions of images requires the global descriptors to be compressed into compact representations. This thesis will focus on how to achieve extremely compact global descriptor representations for large-scale image instance retrieval. After introducing background concepts about supervised neural networks, Restricted Boltzmann Machine (RBM) and deep learning in Chapter 2, Chapter 3 will present the design principles and recent work for the Convolutional Neural Networks (CNN), which recently became the method of choice for large-scale image classification tasks. Next, an original multistage approach for the fusion of the output of multiple CNN is proposed. Submitted as part of the ILSVRC 2014 challenge, results show that this approach can significantly improve classification results. The promising perfor- mance of CNN is largely due to their capability to learn appropriate high-level visual representations from the data. Inspired by a stream of recent works showing that the representations learnt on one particular classification task can transfer well to other classification tasks, subsequent chapters will focus on the transferability of representa- tions learnt by CNN to image instance retrieval…
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Li, Muhua 1973. "Learning invariant neuronal representations for objects across visual-related self-actions." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=85565.

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This work is aimed at understanding and modelling the perceptual stability mechanisms of human visual systems, regardless of large changes in the visual sensory input resulting from some visual-related motions. Invariant neuronal representation plays an important role for memory systems to associate and recognize objects.
In contrast to the bulk of previous research work on the learning of invariance that focuses on the pure bottom-up visual information, we incorporate visual-related self-action signals such as commands for eye, head or body movements, to actively collect the changing visual information and gate the learning process. This helps neural networks learn certain degrees of invariance in an efficient way. We describe a method that can produce a network with invariance to changes in visual input caused by eye movements and covert attention shifts. Training of the network is controlled by signals associated with eye movements and covert attention shifting. A temporal perceptual stability constraint is used to drive the output of the network towards remaining constant across temporal sequences of saccadic motions and covert attention shifts. We use a four-layer neural network model to perform the position-invariant extraction of local features and temporal integration of invariant presentations of local features. The model is further extended to handle viewpoint invariance over eye, head, and/or body movements. We also study cases of multiple features instead of single features in the retinal images, which need a self-organized system to learn over a set of feature classes. A modified saliency map mechanism with spatial constraint is employed to assure that attention stays as much as possible on the same targeted object in a multiple-object scene during the first few shifts.
We present results on both simulated data and real images, to demonstrate that our network can acquire invariant neuronal representations, such as position and attention shift invariance. We also demonstrate that our method performs well in realistic situations in which the temporal sequence of input data is not smooth, situations in which earlier approaches have difficulty.
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Hocke, Jens [Verfasser]. "Representation learning : from feature weighting to invariance / Jens Hocke." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2017. http://d-nb.info/1125057130/34.

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Books on the topic "Invariant representation learning"

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Visual Cortex and Deep Networks: Learning Invariant Representations. The MIT Press, 2016.

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Sejnowski, Terrence J., Tomaso A. Poggio, and Fabio Anselmi. Visual Cortex and Deep Networks: Learning Invariant Representations. MIT Press, 2016.

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Cheng, Patricia W., and Hongjing Lu. Causal Invariance as an Essential Constraint for Creating a Causal Representation of the World. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.9.

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This chapter illustrates the representational nature of causal understanding of the world and examines its implications for causal learning. The vastness of the search space of causal relations, given the representational aspect of the problem, implies that powerful constraints are essential for arriving at adaptive causal relations. The chapter reviews (1) why causal invariance—the sameness of how a causal mechanism operates across contexts—is an essential constraint for causal learning in intuitive reasoning, (2) a psychological causal-learning theory that assumes causal invariance as a defeasible default, (3) some ways in which the computational role of causal invariance in causal learning can become obscured, and (4) the roles of causal invariance as a general aspiration, a default assumption, a criterion for hypothesis revision, and a domain-specific description. The chapter also reviews a puzzling discrepancy in the human and non-human causal and associative learning literatures and offers a potential explanation.
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Book chapters on the topic "Invariant representation learning"

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Shen, Weichao, Yuwei Wu, and Yunde Jia. "Temporal Invariant Factor Disentangled Model for Representation Learning." In Pattern Recognition and Computer Vision, 391–402. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31723-2_33.

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Qin, Shizheng, Kangzheng Gu, Lecheng Wang, Lizhe Qi, and Wenqiang Zhang. "Learning Camera-Invariant Representation for Person Re-identification." In Lecture Notes in Computer Science, 125–37. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30484-3_11.

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Aritake, Toshimitsu, and Noboru Murata. "Learning Scale and Shift-Invariant Dictionary for Sparse Representation." In Machine Learning, Optimization, and Data Science, 472–83. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_39.

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Li, Muhua, and James J. Clark. "Learning of Position-Invariant Object Representation Across Attention Shifts." In Lecture Notes in Computer Science, 57–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30572-9_5.

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Shtrosberg, Aviad, Jesus Villalba, Najim Dehak, Azaria Cohen, and Bar Ben-Yair. "Invariant Representation Learning for Robust Far-Field Speaker Recognition." In Statistical Language and Speech Processing, 97–110. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89579-2_9.

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Huang, Junqiang, Xiangwen Kong, and Xiangyu Zhang. "Revisiting the Critical Factors of Augmentation-Invariant Representation Learning." In Lecture Notes in Computer Science, 42–58. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19821-2_3.

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Bouajjani, Ahmed, Wael-Amine Boutglay, and Peter Habermehl. "Data-driven Numerical Invariant Synthesis with Automatic Generation of Attributes." In Computer Aided Verification, 282–303. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13185-1_14.

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AbstractWe propose a data-driven algorithm for numerical invariant synthesis and verification. The algorithm is based on the ICE-DT schema for learning decision trees from samples of positive and negative states and implications corresponding to program transitions. The main issue we address is the discovery of relevant attributes to be used in the learning process of numerical invariants. We define a method for solving this problem guided by the data sample. It is based on the construction of a separator that covers positive states and excludes negative ones, consistent with the implications. The separator is constructed using an abstract domain representation of convex sets. The generalization mechanism of the decision tree learning from the constraints of the separator allows the inference of general invariants, accurate enough for proving the targeted property. We implemented our algorithm and showed its efficiency.
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Iosifidis, Alexandros, Anastasios Tefas, Nikolaos Nikolaidis, and Ioannis Pitas. "Learning Human Identity Using View-Invariant Multi-view Movement Representation." In Lecture Notes in Computer Science, 217–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19530-3_20.

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Ghimire, Sandesh, Satyananda Kashyap, Joy T. Wu, Alexandros Karargyris, and Mehdi Moradi. "Learning Invariant Feature Representation to Improve Generalization Across Chest X-Ray Datasets." In Machine Learning in Medical Imaging, 644–53. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59861-7_65.

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Zhao, Qing, Huimin Ma, Ruiqi Lu, Yanxian Chen, and Dong Li. "MVAD-Net: Learning View-Aware and Domain-Invariant Representation for Baggage Re-identification." In Pattern Recognition and Computer Vision, 142–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88004-0_12.

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Conference papers on the topic "Invariant representation learning"

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Du, Xiaoyu, Zike Wu, Fuli Feng, Xiangnan He, and Jinhui Tang. "Invariant Representation Learning for Multimedia Recommendation." In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548405.

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Du, Wenchao, Hu Chen, and Hongyu Yang. "Learning Invariant Representation for Unsupervised Image Restoration." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01449.

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Li, Yi, Cornelia Fermuller, Yiannis Aloimonos, and Hui Ji. "Learning shift-invariant sparse representation of actions." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539977.

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Li, Haoqi, Ming Tu, Jing Huang, Shrikanth Narayanan, and Panayiotis Georgiou. "Speaker-Invariant Affective Representation Learning via Adversarial Training." In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020. http://dx.doi.org/10.1109/icassp40776.2020.9054580.

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Ma, Chao, Xiaokang Yang, Chongyang Zhang, and Ming-Hsuan Yang. "Learning a temporally invariant representation for visual tracking." In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015. http://dx.doi.org/10.1109/icip.2015.7350921.

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Rayatdoost, Soheil, Yufeng Yin, David Rudrauf, and Mohammad Soleymani. "Subject-Invariant Eeg Representation Learning For Emotion Recognition." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414496.

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Li, Zongmin, Yupeng Zhang, and Yun Bai. "Geometric Invariant Representation Learning for 3D Point Cloud." In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00235.

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Tran, Luan, Xi Yin, and Xiaoming Liu. "Disentangled Representation Learning GAN for Pose-Invariant Face Recognition." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.141.

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Chen, Jiawei, Janusz Konrad, and Prakash Ishwar. "A Cyclically-Trained Adversarial Network for Invariant Representation Learning." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2020. http://dx.doi.org/10.1109/cvprw50498.2020.00399.

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Jeong, Seong-Yun, Ho-Joong Kim, Myeong-Seok Oh, Gun-Hee Lee, and Seong-Whan Lee. "Temporal-Invariant Video Representation Learning with Dynamic Temporal Resolutions." In 2022 18th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2022. http://dx.doi.org/10.1109/avss56176.2022.9959310.

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