Дисертації з теми "Sparse Deep Learning"
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Tavanaei, Amirhossein. "Spiking Neural Networks and Sparse Deep Learning." Thesis, University of Louisiana at Lafayette, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10807940.
Повний текст джерелаThis document proposes new methods for training multi-layer and deep spiking neural networks (SNNs), specifically, spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly used backpropagation method for non-spiking networks is not easily applied. Our methods use novel versions of the brain-like, local learning rule named spike-timing-dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and converts them to spatio-temporally local rules suited for SNNs.
The training uses two components for unsupervised feature extraction and supervised classification. The first component refers to new STDP rules for spike-based representation learning that trains convolutional filters and initial representations. The second introduces new STDP-based supervised learning rules for spike pattern classification via an approximation to gradient descent by combining the STDP and anti-STDP rules. Specifically, the STDP-based supervised learning model approximates gradient descent by using temporally local STDP rules. Stacking these components implements a novel sparse, spiking deep learning model. Our spiking deep learning model is categorized as a variation of spiking CNNs of integrate-and-fire (IF) neurons with performance comparable with the state-of-the-art deep SNNs. The experimental results show the success of the proposed model for image classification. Our network architecture is the only spiking CNN which provides bio-inspired STDP rules in a hierarchy of feature extraction and classification in an entirely spike-based framework.
Beretta, Davide. "Experience Replay in Sparse Rewards Problems using Deep Reinforcement Techniques." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17531/.
Повний текст джерелаBenini, Francesco. "Predicting death in games with deep reinforcement learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20755/.
Повний текст джерелаVekhande, Swapnil Sudhir. "Deep Learning Neural Network-based Sinogram Interpolation for Sparse-View CT Reconstruction." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90182.
Повний текст джерелаMaster of Science
Computed Tomography is a commonly used imaging technique due to the remarkable ability to visualize internal organs, bones, soft tissues, and blood vessels. It involves exposing the subject to X-ray radiation, which could lead to cancer. On the other hand, the radiation dose is critical for the image quality and subsequent diagnosis. Thus, image reconstruction using only a small number of projection data is an open research problem. Deep learning techniques have already revolutionized various Computer Vision applications. Here, we have used a method which fills missing highly sparse CT data. The results show that the deep learning-based method outperforms standard linear interpolation-based methods while improving the image quality.
Hoori, Ammar O. "MULTI-COLUMN NEURAL NETWORKS AND SPARSE CODING NOVEL TECHNIQUES IN MACHINE LEARNING." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5743.
Повний текст джерелаBonfiglioli, Luca. "Identificazione efficiente di reti neurali sparse basata sulla Lottery Ticket Hypothesis." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Знайти повний текст джерелаPawlowski, Filip igor. "High-performance dense tensor and sparse matrix kernels for machine learning." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN081.
Повний текст джерелаIn this thesis, we develop high performance algorithms for certain computations involving dense tensors and sparse matrices. We address kernel operations that are useful for machine learning tasks, such as inference with deep neural networks (DNNs). We develop data structures and techniques to reduce memory use, to improve data locality and hence to improve cache reuse of the kernel operations. We design both sequential and shared-memory parallel algorithms. In the first part of the thesis we focus on dense tensors kernels. Tensor kernels include the tensor--vector multiplication (TVM), tensor--matrix multiplication (TMM), and tensor--tensor multiplication (TTM). Among these, TVM is the most bandwidth-bound and constitutes a building block for many algorithms. We focus on this operation and develop a data structure and sequential and parallel algorithms for it. We propose a novel data structure which stores the tensor as blocks, which are ordered using the space-filling curve known as the Morton curve (or Z-curve). The key idea consists of dividing the tensor into blocks small enough to fit cache, and storing them according to the Morton order, while keeping a simple, multi-dimensional order on the individual elements within them. Thus, high performance BLAS routines can be used as microkernels for each block. We evaluate our techniques on a set of experiments. The results not only demonstrate superior performance of the proposed approach over the state-of-the-art variants by up to 18%, but also show that the proposed approach induces 71% less sample standard deviation for the TVM across the d possible modes. Finally, we show that our data structure naturally expands to other tensor kernels by demonstrating that it yields up to 38% higher performance for the higher-order power method. Finally, we investigate shared-memory parallel TVM algorithms which use the proposed data structure. Several alternative parallel algorithms were characterized theoretically and implemented using OpenMP to compare them experimentally. Our results on up to 8 socket systems show near peak performance for the proposed algorithm for 2, 3, 4, and 5-dimensional tensors. In the second part of the thesis, we explore the sparse computations in neural networks focusing on the high-performance sparse deep inference problem. The sparse DNN inference is the task of using sparse DNN networks to classify a batch of data elements forming, in our case, a sparse feature matrix. The performance of sparse inference hinges on efficient parallelization of the sparse matrix--sparse matrix multiplication (SpGEMM) repeated for each layer in the inference function. We first characterize efficient sequential SpGEMM algorithms for our use case. We then introduce the model-parallel inference, which uses a two-dimensional partitioning of the weight matrices obtained using the hypergraph partitioning software. The model-parallel variant uses barriers to synchronize at layers. Finally, we introduce tiling model-parallel and tiling hybrid algorithms, which increase cache reuse between the layers, and use a weak synchronization module to hide load imbalance and synchronization costs. We evaluate our techniques on the large network data from the IEEE HPEC 2019 Graph Challenge on shared-memory systems and report up to 2x times speed-up versus the baseline
Abbasnejad, Iman. "Learning spatio-temporal features for efficient event detection." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/121184/1/Iman_Abbasnejad_Thesis.pdf.
Повний текст джерелаMöckelind, Christoffer. "Improving deep monocular depth predictions using dense narrow field of view depth images." Thesis, KTH, Robotik, perception och lärande, RPL, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235660.
Повний текст джерелаI det här arbetet studerar vi ett djupapproximationsproblem där vi tillhandahåller en djupbild med smal synvinkel och en RGB-bild med bred synvinkel till ett djupt nätverk med uppgift att förutsäga djupet för hela RGB-bilden. Vi visar att genom att ge djupbilden till nätverket förbättras resultatet för området utanför det tillhandahållna djupet jämfört med en existerande metod som använder en RGB-bild för att förutsäga djupet. Vi undersöker flera arkitekturer och storlekar på djupbildssynfält och studerar effekten av att lägga till brus och sänka upplösningen på djupbilden. Vi visar att större synfält för djupbilden ger en större fördel och även att modellens noggrannhet minskar med avståndet från det angivna djupet. Våra resultat visar också att modellerna som använde sig av det brusiga lågupplösta djupet presterade på samma nivå som de modeller som använde sig av det omodifierade djupet.
Moreau, Thomas. "Représentations Convolutives Parcimonieuses -- application aux signaux physiologiques et interpétabilité de l'apprentissage profond." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLN054/document.
Повний текст джерелаConvolutional representations extract recurrent patterns which lead to the discovery of local structures in a set of signals. They are well suited to analyze physiological signals which requires interpretable representations in order to understand the relevant information. Moreover, these representations can be linked to deep learning models, as a way to bring interpretability intheir internal representations. In this disserta tion, we describe recent advances on both computational and theoretical aspects of these models.First, we show that the Singular Spectrum Analysis can be used to compute convolutional representations. This representation is dense and we describe an automatized procedure to improve its interpretability. Also, we propose an asynchronous algorithm, called DICOD, based on greedy coordinate descent, to solve convolutional sparse coding for long signals. Our algorithm has super-linear acceleration.In a second part, we focus on the link between representations and neural networks. An extra training step for deep learning, called post-training, is introduced to boost the performances of the trained network by making sure the last layer is optimal. Then, we study the mechanisms which allow to accelerate sparse coding algorithms with neural networks. We show that it is linked to afactorization of the Gram matrix of the dictionary.Finally, we illustrate the relevance of convolutional representations for physiological signals. Convolutional dictionary learning is used to summarize human walk signals and Singular Spectrum Analysis is used to remove the gaze movement in young infant’s oculometric recordings
Carvalho, Micael. "Deep representation spaces." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS292.
Повний текст джерелаIn recent years, Deep Learning techniques have swept the state-of-the-art of many applications of Machine Learning, becoming the new standard approach for them. The architectures issued from these techniques have been used for transfer learning, which extended the power of deep models to tasks that did not have enough data to fully train them from scratch. This thesis' subject of study is the representation spaces created by deep architectures. First, we study properties inherent to them, with particular interest in dimensionality redundancy and precision of their features. Our findings reveal a strong degree of robustness, pointing the path to simple and powerful compression schemes. Then, we focus on refining these representations. We choose to adopt a cross-modal multi-task problem, and design a loss function capable of taking advantage of data coming from multiple modalities, while also taking into account different tasks associated to the same dataset. In order to correctly balance these losses, we also we develop a new sampling scheme that only takes into account examples contributing to the learning phase, i.e. those having a positive loss. Finally, we test our approach in a large-scale dataset of cooking recipes and associated pictures. Our method achieves a 5-fold improvement over the state-of-the-art, and we show that the multi-task aspect of our approach promotes a semantically meaningful organization of the representation space, allowing it to perform subtasks never seen during training, like ingredient exclusion and selection. The results we present in this thesis open many possibilities, including feature compression for remote applications, robust multi-modal and multi-task learning, and feature space refinement. For the cooking application, in particular, many of our findings are directly applicable in a real-world context, especially for the detection of allergens, finding alternative recipes due to dietary restrictions, and menu planning
Cherief-Abdellatif, Badr-Eddine. "Contributions to the theoretical study of variational inference and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG001.
Повний текст джерелаThis PhD thesis deals with variational inference and robustness. More precisely, it focuses on the statistical properties of variational approximations and the design of efficient algorithms for computing them in an online fashion, and investigates Maximum Mean Discrepancy based estimators as learning rules that are robust to model misspecification.In recent years, variational inference has been extensively studied from the computational viewpoint, but only little attention has been put in the literature towards theoretical properties of variational approximations until very recently. In this thesis, we investigate the consistency of variational approximations in various statistical models and the conditions that ensure the consistency of variational approximations. In particular, we tackle the special case of mixture models and deep neural networks. We also justify in theory the use of the ELBO maximization strategy, a model selection criterion that is widely used in the Variational Bayes community and is known to work well in practice.Moreover, Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even under model mismatch and with adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference? In this thesis, we show that this is indeed the case for some variational inference algorithms. We propose new online, tempered variational algorithms and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that our result should hold more generally and present empirical evidence in support of this. Our work presents theoretical justifications in favor of online algorithms that rely on approximate Bayesian methods. Another point that is addressed in this thesis is the design of a universal estimation procedure. This question is of major interest, in particular because it leads to robust estimators, a very hot topic in statistics and machine learning. We tackle the problem of universal estimation using a minimum distance estimator based on the Maximum Mean Discrepancy. We show that the estimator is robust to both dependence and to the presence of outliers in the dataset. We also highlight the connections that may exist with minimum distance estimators using L2-distance. Finally, we provide a theoretical study of the stochastic gradient descent algorithm used to compute the estimator, and we support our findings with numerical simulations. We also propose a Bayesian version of our estimator, that we study from both a theoretical and a computational points of view
Al, chanti Dawood. "Analyse Automatique des Macro et Micro Expressions Faciales : Détection et Reconnaissance par Machine Learning." Thesis, Université Grenoble Alpes (ComUE), 2019. http://www.theses.fr/2019GREAT058.
Повний текст джерелаFacial expression analysis is an important problem in many biometric tasks, such as face recognition, face animation, affective computing and human computer interface. In this thesis, we aim at analyzing facial expressions of a face using images and video sequences. We divided the problem into three leading parts.First, we study Macro Facial Expressions for Emotion Recognition and we propose three different levels of feature representations. Low-level feature through a Bag of Visual Word model, mid-level feature through Sparse Representation and hierarchical features through a Deep Learning based method. The objective of doing this is to find the most effective and efficient representation that contains distinctive information of expressions and that overcomes various challenges coming from: 1) intrinsic factors such as appearance and expressiveness variability and 2) extrinsic factors such as illumination, pose, scale and imaging parameters, e.g., resolution, focus, imaging, noise. Then, we incorporate the time dimension to extract spatio-temporal features with the objective to describe subtle feature deformations to discriminate ambiguous classes.Second, we direct our research toward transfer learning, where we aim at Adapting Facial Expression Category Models to New Domains and Tasks. Thus we study domain adaptation and zero shot learning for developing a method that solves the two tasks jointly. Our method is suitable for unlabelled target datasets coming from different data distributions than the source domain and for unlabelled target datasets with different label distributions but sharing the same context as the source domain. Therefore, to permit knowledge transfer between domains and tasks, we use Euclidean learning and Convolutional Neural Networks to design a mapping function that map the visual information coming from facial expressions into a semantic space coming from a Natural Language model that encodes the visual attribute description or use the label information. The consistency between the two subspaces is maximized by aligning them using the visual feature distribution.Third, we study Micro Facial Expression Detection. We propose an algorithm to spot micro-expression segments including the onset and offset frames and to spatially pinpoint in each image space the regions involved in the micro-facial muscle movements. The problem is formulated into Anomaly Detection due to the fact that micro-expressions occur infrequently and thus leading to few data generation compared to natural facial behaviours. In this manner, first, we propose a deep Recurrent Convolutional Auto-Encoder to capture spatial and motion feature changes of natural facial behaviours. Then, a statistical based model for estimating the probability density function of normal facial behaviours while associating a discriminating score to spot micro-expressions is learned based on a Gaussian Mixture Model. Finally, an adaptive thresholding technique for identifying micro expressions from natural facial behaviour is proposed.Our algorithms are tested over deliberate and spontaneous facial expression benchmarks
Rossini, Eugenio. "Deep Learning and Nonlinear PDEs in High-Dimensional Spaces." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16859/.
Повний текст джерелаRastgoufard, Rastin. "Multi-Label Latent Spaces with Semi-Supervised Deep Generative Models." ScholarWorks@UNO, 2018. https://scholarworks.uno.edu/td/2486.
Повний текст джерелаPallotti, Davide. "Integrazione di dati di disparità sparsi in algoritmi per la visione stereo basati su deep-learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16633/.
Повний текст джерелаMehr, Éloi. "Unsupervised Learning of 3D Shape Spaces for 3D Modeling." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS566.
Повний текст джерелаEven though 3D data is becoming increasingly more popular, especially with the democratization of virtual and augmented experiences, it remains very difficult to manipulate a 3D shape, even for designers or experts. Given a database containing 3D instances of one or several categories of objects, we want to learn the manifold of plausible shapes in order to develop new intelligent 3D modeling and editing tools. However, this manifold is often much more complex compared to the 2D domain. Indeed, 3D surfaces can be represented using various embeddings, and may also exhibit different alignments and topologies. In this thesis we study the manifold of plausible shapes in the light of the aforementioned challenges, by deepening three different points of view. First of all, we consider the manifold as a quotient space, in order to learn the shapes’ intrinsic geometry from a dataset where the 3D models are not co-aligned. Then, we assume that the manifold is disconnected, which leads to a new deep learning model that is able to automatically cluster and learn the shapes according to their typology. Finally, we study the conversion of an unstructured 3D input to an exact geometry, represented as a structured tree of continuous solid primitives
Chi, Lu-cheng, and 紀律呈. "An Improved Deep Reinforcement Learning with Sparse Rewards." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/eq94pr.
Повний текст джерела國立中山大學
電機工程學系研究所
107
In reinforcement learning, how an agent explores in an environment with sparse rewards is a long-standing problem. An improved deep reinforcement learning described in this thesis encourages an agent to explore unvisited environmental states in an environment with sparse rewards. In deep reinforcement learning, an agent directly uses an image observation from environment as an input to the neural network. However, some neglected observations from environment, such as depth, might provide valuable information. An improved deep reinforcement learning described in this thesis is based on the Actor-Critic algorithm and uses the convolutional neural network as a hetero-encoder between an image input and other observations from environment. In the environment with sparse rewards, we use these neglected observations from environment as a target output of supervised learning and provide an agent denser training signals through supervised learning to bootstrap reinforcement learning. In addition, we use the loss from supervised learning as the feedback for an agent’s exploration behavior in an environment, called the label reward, to encourage an agent to explore unvisited environmental states. Finally, we construct multiple neural networks by Asynchronous Advantage Actor-Critic algorithm and learn the policy with multiple agents. An improved deep reinforcement learning described in this thesis is compared with other deep reinforcement learning in an environment with sparse rewards and achieves better performance.
YANG, FU-MING, and 楊富名. "Sparse Matrix Based Image Enhancement for Target Detection using Deep Learning." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/68a2ej.
Повний текст джерела國立雲林科技大學
資訊工程系
105
In many paper, it developed algorithm to target detection which can be divide into supervised and unsupervised approaches. This thesis used hyper algorithm and convolution neural network to conduct target detection. The method of feature extraction used filter to scan the image and input this powerful feature to the neural network classifier. Support vector machine can find the hyperplane to classify target in the image. Linear discriminant analysis can project the data to low dimension that divide the data into two class precisely. In order to raise the result of choosing wrong target, it can be use image pre-processing to enhance the target in the image. It can use Robust PCA to divide the image into Low-rank matrix and Sparse matrix. Sparse matrix is the noise in the image and maybe represent the target in the image. Therefore, this thesis proposed combination with sparse matrix that can promote the outcome. The experiments show that can use image pre-processing to get target and effectively raise the result of choosing wrong target.
Peng, Yu-Shao, and 彭宇劭. "Exploring Sparse Features in Deep Reinforcement Learning towards Fast Disease Diagnosis." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/43q7bz.
Повний текст джерела國立臺灣大學
資訊工程學研究所
106
Self disease diagnosis is becoming more and more important in recent years. Irrespective of whether the area has sufficient medical resources or not, the system for self disease diagnosis plays an important role. Although patients can conveniently search medical information from the internet, search results are often inaccurate. A robust system for self disease diagnosis needs to provide accurate prediction and fast disease diagnosis. However, these two properties are in conflict with each other. In addition, only a few symptoms per patient appear in a disease, that is, the feature space is sparse. The previous methods do not consider the sparse feature problem and result in inferior performance when dealing with a large number of possible diseases. In this work, we formulate the disease diagnosis problem as a sequential decision making process, and propose a reinforcement learning algorithm RE^2 to improve the performance of self disease diagnosis. To overcome the sparse feature problem, we propose a reward shaping technique and a reconstruction technique in RE^2. Reward shaping can guide the search towards symptoms that actually appear on the patient. Reconstruction can guide the agent to learn correlations between symptoms. Together, they can find symptom queries that yield key positive responses from a patient with high probability. Consequently, by using these techniques, the agent can obtain much improved diagnoses and state-of-the-art results in different experimental settings.
CHUANG, HSIANG-LUNG, and 莊翔隆. "Deep Learning of Approximate Message Passing Algorithm based on Sparse Superposition Code." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9n346y.
Повний текст джерела國立中正大學
通訊工程研究所
107
In coding and information theory, it has been a crucial issue to develop a computationally efficient, capacity-achieving code. This paper is based on two coding schemes. One is Sparse Superposition Codes (or Sparse Regression Codes) and the other one is Approximate Message Passing Algorithm. Especially for Approximate Message Passing Algorithm, we will make some improvement and optimization to it through machine learning. Under additive white Gaussian noise channel (AWGN) and power constraint circumstance, not only is Sparse Superposition Codes both feasible in encoding and decoding, but it can also nearly achieve channel capacity. Its codewords are defined by a matrix which is Gaussian distributed and will be divided into several sections. By selecting some column vectors from every section to do linear combination, we can generate its codewords. During iterative decoding, Approximate Message Passing Algorithm will process many matrix multiplications and quantization and it is very similar to the operation of deep learning scheme, which can easily realize by constructing neuron networks. However, traditional Approximate Message Passing Algorithm needs to prepare many parameters before computation. Thus, it is very possible to improve it by deep learning. Finally, in this paper, we propose two different ways to deal with different length of codewords.
Srinivas, Suraj. "Learning Compact Architectures for Deep Neural Networks." Thesis, 2017. http://etd.iisc.ernet.in/2005/3581.
Повний текст джерела(8892395), Yao Chen. "Inferential GANs and Deep Feature Selection with Applications." Thesis, 2020.
Знайти повний текст джерела"Towards Developing Computer Vision Algorithms and Architectures for Real-world Applications." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.51662.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Computer Science 2018
Goodfellow, Ian. "Deep learning of representations and its application to computer vision." Thèse, 2014. http://hdl.handle.net/1866/11674.
Повний текст джерела"Deep Active Learning Explored Across Diverse Label Spaces." Doctoral diss., 2018. http://hdl.handle.net/2286/R.I.49076.
Повний текст джерелаDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2018
(9193706), Michael Siyuan Wang. "Evaluating Tangent Spaces, Distances, and Deep Learning Models to Develop Classifiers for Brain Connectivity Data." Thesis, 2020.
Знайти повний текст джерелаTouati, Redha. "Détection de changement en imagerie satellitaire multimodale." Thèse, 2019. http://hdl.handle.net/1866/22662.
Повний текст джерелаCette recherche a pour objet l’étude de la détection de changements temporels entre deux (ou plusieurs) images satellitaires multimodales, i.e., avec deux modalités d’imagerie différentes acquises par deux capteurs hétérogènes donnant pour la même scène deux images encodées différemment suivant la nature du capteur utilisé pour chacune des prises de vues. Les deux (ou multiples) images satellitaires multimodales sont prises et co-enregistrées à deux dates différentes, avant et après un événement. Dans le cadre de cette étude, nous proposons des nouveaux modèles de détection de changement en imagerie satellitaire multimodale semi ou non supervisés. Comme première contribution, nous présentons un nouveau scénario de contraintes exprimé sur chaque paire de pixels existant dans l’image avant et après changement. Une deuxième contribution de notre travail consiste à proposer un opérateur de gradient textural spatio-temporel exprimé avec des normes complémentaires ainsi qu’une nouvelle stratégie de dé-bruitage de la carte de différence issue de cet opérateur. Une autre contribution consiste à construire un champ d’observation à partir d’une modélisation par paires de pixels et proposer une solution au sens du maximum a posteriori. Une quatrième contribution est proposée et consiste à construire un espace commun de caractéristiques pour les deux images hétérogènes. Notre cinquième contribution réside dans la modélisation des zones de changement comme étant des anomalies et sur l’analyse des erreurs de reconstruction dont nous proposons d’apprendre un modèle non-supervisé à partir d’une base d’apprentissage constituée seulement de zones de non-changement afin que le modèle reconstruit les motifs de non-changement avec une faible erreur. Dans la dernière contribution, nous proposons une architecture d’apprentissage par paires de pixels basée sur un réseau CNN pseudo-siamois qui prend en entrée une paire de données au lieu d’une seule donnée et est constituée de deux flux de réseau (descripteur) CNN parallèles et partiellement non-couplés suivis d’un réseau de décision qui comprend de couche de fusion et une couche de classification au sens du critère d’entropie. Les modèles proposés s’avèrent assez flexibles pour être utilisés efficacement dans le cas des données-images mono-modales.
Zumer, Jeremie. "Influencing the Properties of Latent Spaces." Thèse, 2016. http://hdl.handle.net/1866/18767.
Повний текст джерела