Relevant bibliographies by topics / Domain generalisation

Academic literature on the topic 'Domain generalisation'

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Published: 22 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Journal articles on the topic "Domain generalisation":

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Zhou, Kaiyang, Yongxin Yang, Timothy Hospedales, and Tao Xiang. "Deep Domain-Adversarial Image Generation for Domain Generalisation." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 13025–32. http://dx.doi.org/10.1609/aaai.v34i07.7003.

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Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution. To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains. In this paper, we propose a novel DG approach based on Deep Domain-Adversarial Image Generation (DDAIG). Specifically, DDAIG consists of three components, namely a label classifier, a domain classifier and a domain transformation network (DoTNet). The goal for DoTNet is to map the source training data to unseen domains. This is achieved by having a learning objective formulated to ensure that the generated data can be correctly classified by the label classifier while fooling the domain classifier. By augmenting the source training data with the generated unseen domain data, we can make the label classifier more robust to unknown domain changes. Extensive experiments on four DG datasets demonstrate the effectiveness of our approach.
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Seemakurthy, Karthik, Charles Fox, Erchan Aptoula, and Petra Bosilj. "Domain Generalised Faster R-CNN." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (June 26, 2023): 2180–90. http://dx.doi.org/10.1609/aaai.v37i2.25312.

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Domain generalisation (i.e. out-of-distribution generalisation) is an open problem in machine learning, where the goal is to train a model via one or more source domains, that will generalise well to unknown target domains. While the topic is attracting increasing interest, it has not been studied in detail in the context of object detection. The established approaches all operate under the covariate shift assumption, where the conditional distributions are assumed to be approximately equal across source domains. This is the first paper to address domain generalisation in the context of object detection, with a rigorous mathematical analysis of domain shift, without the covariate shift assumption. We focus on improving the generalisation ability of object detection by proposing new regularisation terms to address the domain shift that arises due to both classification and bounding box regression. Also, we include an additional consistency regularisation term to align the local and global level predictions. The proposed approach is implemented as a Domain Generalised Faster R-CNN and evaluated using four object detection datasets which provide domain metadata (GWHD, Cityscapes, BDD100K, Sim10K) where it exhibits a consistent performance improvement over the baselines. All the codes for replicating the results in this paper can be found at https://github.com/karthikiitm87/domain-generalisation.git
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Le, Hoang Son, Rini Akmeliawati, and Gustavo Carneiro. "Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (May 18, 2021): 15821–22. http://dx.doi.org/10.1609/aaai.v35i18.17907.

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Domain generalisation (DG) methods address the problem of domain shift, when there is a mismatch between the distributions of training and target domains. Data augmentation approaches have emerged as a promising alternative for DG. However, data augmentation alone is not sufficient to achieve lower generalisation errors. This project proposes a new method that combines data augmentation and domain distance minimisation to address the problems associated with data augmentation and provide a guarantee on the learning performance, under an existing framework. Empirically, our method outperforms baseline results on DG benchmarks.
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Roeper, P. "Generalisation of first-order logic to nonatomic domains." Journal of Symbolic Logic 50, no. 3 (September 1985): 815–38. http://dx.doi.org/10.2307/2274334.

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The quantifiers of standard predicate logic are interpreted as ranging over domains of individuals, and interpreted formulae beginning with a quantifier make claims to the effect that something is true of every individual, i.e. of the whole domain, or of some individuals, i.e. of part of the domain. To state that something is true of all or part of a totality seems to be the basic significance of universal and existential quantification, and this by itself does not involve a specification of the structure of the totality. This means that the notion of quantification by itself does not demand totalities of individuals, i.e. atomic totalities, as domains of quantification. Nonatomic domains, such as volumes of space, or surfaces, are equally in order. So one might say that a certain predicate applies “everywhere” or “somewhere” in such a domain. All that the concept of quantification requires is a totality which is structured in terms of a part-to-whole relation, and appropriate properties that apply to part or all of the totality. Quantification does not demand that the totality have smallest parts, or atoms. There is no conflict with the sense of universal or existential quantification if the domain is nonatomic, if every one of its parts has itself proper parts.The most general kind of quantification theory must then deal with totalities of any kind, atomic or not. The relationships among the parts of a domain are described by the theory of Boolean algebras, which we can regard as the most general characterisation of a totality, of a domain of quantification.In this paper I shall be concerned with this generalised theory of quantification, which encompasses nonatomic domains as well as atomic and mixed domains, i.e. totalities consisting entirely or partly of individuals.
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Gomathi, R., and S. Selvakumaran. "A Novel Medical Image Segmentation Model with Domain Generalization Approach." International Journal of Electrical and Electronics Research 10, no. 2 (June 30, 2022): 312–19. http://dx.doi.org/10.37391/ijeer.100242.

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In deep learning-based computing vision for image processing, image segmentation is a prominent issue. There is promising generalisation performance in the medical image segmentation sector for approaches using domain generalisation (DG). Single domain generalisation (SDG) is a more difficult problem than conventional generalisation (DG), which requires numerous source domains to be accessible during network training, as opposed to conventional generalisation (DG). Color medical images may be incorrectly segmented because of the augmentation of the full image in order to increase model generalisation capacity. An arbitrary illumination SDG model for improving generalisation power for colour image segmentation approach for medical images through synthesizing random radiance charts is presented as a first solution to this challenge. Color medical images may be decomposed into reflectivity and illumination maps using retinex-based neural networks (ID-Nets). In order to provide medical colour images under various lighting situations, illumination randomization is used to enhance illumination maps. A new metric, TGCI, called the transfer gradient consistency index was devised to quantify the performance of the breakdown of retinal images by simulating physical lighting. Two of the existing retinal image segmentation tasks are tested extensively in order to assess our suggested system. According to the Dice coefficient, our framework surpasses previous SDGs and image improvement algorithms, outperforming the best SDGs by up to 1.7 per cent.
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Myers, Scott, and Jaye Padgett. "Domain generalisation in artificial language learning." Phonology 31, no. 3 (December 2014): 399–433. http://dx.doi.org/10.1017/s0952675714000207.

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Many languages have restrictions on word-final segments, such as a requirement that any word-final obstruent be voiceless. There is a phonetic basis for such restrictions at the ends of utterances, but not the ends of words. Historical linguists have long noted this mismatch, and have attributed it to an analogical generalisation of such restrictions from utterance-final to word-final position. To test whether language learners actually generalise in this way, two artificial language learning experiments were conducted. Participants heard nonsense utterances in which there was a restriction on utterance-final obstruents, but in which no information was available about word-final utterance-medial obstruents. They were then tested on utterances that included obstruents in both positions. They learned the pattern and generalised it to word-final utterance-medial position, confirming that learners are biased toward word-based distributional patterns.
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HYLAND, MARTIN. "Some reasons for generalising domain theory." Mathematical Structures in Computer Science 20, no. 2 (March 25, 2010): 239–65. http://dx.doi.org/10.1017/s0960129509990375.

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One natural way to generalise domain theory is to replace partially ordered sets by categories. This kind of generalisation has recently found application in the study of concurrency. An outline is given of the elegant mathematical foundations that have been developed. This is specialised to give a construction of cartesian closed categories of domains, which throws light on standard presentations of domain theory.
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Rashid, Fariza, Ben Doyle, Soyeon Caren Han, and Suranga Seneviratne. "Phishing URL detection generalisation using Unsupervised Domain Adaptation." Computer Networks 245 (May 2024): 110398. http://dx.doi.org/10.1016/j.comnet.2024.110398.

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Anthony, Martin, Peter Bartlett, Yuval Ishai, and John Shawe-Taylor. "Valid Generalisation from Approximate Interpolation." Combinatorics, Probability and Computing 5, no. 3 (September 1996): 191–214. http://dx.doi.org/10.1017/s096354830000198x.

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Let and be sets of functions from domain X to ℝ. We say that validly generalises from approximate interpolation if and only if for each η > 0 and ∈, δ ∈ (0,1) there is m0(η, ∈, δ) such that for any function t ∈ and any probability distribution on X, if m > m0 then with m-probability at least 1 – δ, a sample X = (x1, X2,…,xm) ∈ Xm satisfiesWe find conditions that are necessary and sufficient for to validly generalise from approximate interpolation, and we obtain bounds on the sample length m0{η,∈,δ) in terms of various parameters describing the expressive power of .
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Orazov, B. B. "On the asymptotic behaviour at infinity of solutions of the traction boundary value problem." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 111, no. 1-2 (1989): 33–52. http://dx.doi.org/10.1017/s0308210500024999.

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SynopsisKorn's inequalities are proved for star-shaped domains and it is shown how the constants in these inequalities depend on the dimensions of the domain. These inequalities are then used to prove a generalisation of Saint-Venant's Principle for nonlinear elasticity and additionally to establish the asymptotic behaviour of solutions to the traction boundary value problem for a non-prismatic cylinder.
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Journal articles

Dissertations / Theses on the topic "Domain generalisation":

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Rahman, Mohammad Mahfujur. "Deep domain adaptation and generalisation." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/205619/1/Mohammad%20Mahfujur_Rahman_Thesis.pdf.

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This thesis addresses a critical problem in computer vision of dealing with dataset bias between source and target environments. Variations in image data can arise from multiple factors including contrasts in picture quality (shading, brightness, colour, resolution, and occlusion), diverse backgrounds, distinct circumstances, changes in camera viewpoint, and implicit heterogeneity of the samples themselves. This research developed strategies to address this domain shift problem for the object recognition task. Several domain adaptation and generalization approaches based on deep neural networks were introduced to improve poor performance due to domain shift or domain bias.
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McLean, David. "Improving generalisation in continuous data domains." Thesis, University of Manchester, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283816.

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Guesdon, Romain. "Estimation de poses humaines par apprentissage profond : application aux passagers des véhicules autonomes." Electronic Thesis or Diss., Lyon 2, 2024. http://www.theses.fr/2024LYO20002.

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La recherche concernant les voitures autonomes a fortement progressé ces dernières décennies, en se concentrant particulièrement sur l'analyse de l'environnement extérieur et sur les tâches liées à la conduite. Cela a permis une importante croissance de l'autonomie des véhicules particuliers. Dans ce nouveau contexte, il peut être pertinent de s'intéresser aux passagers de ces véhicules autonomes afin d'étudier le comportement de ces derniers face à cette révolution du moyen de transport. C'est pour approfondir ces thématiques que le projet région AURA AutoBehave a été mis en place. Ce projet réunit plusieurs laboratoires menant des recherches dans différentes disciplines scientifiques liées à cette thématique telles que la vision par ordinateur, la biomécanique, les émotions ou encore l'économie des transports. Cette thèse menée au laboratoire LIRIS s'inscrit donc dans ce projet, dans laquelle nous nous intéressons aux méthodes d'estimation de poses humaines des passagers par apprentissage profond. Nous avons d'abord étudié les solutions de l'état de l'art, et avons développé un jeu de données ainsi qu'une métrique plus adaptée aux contraintes de notre contexte. Nous nous sommes également intéressés à la visibilité des points afin d'aider l'estimation de la pose. Par la suite, nous nous sommes attaqués à la problématique de généralisation de domaine pour l'estimation de poses dans le but de proposer une solution efficace dans des conditions inconnues. Ainsi, nous nous sommes intéressés à la génération de données synthétiques de passagers pour l'estimation de poses afin de combler le manque de jeux de données annotés disponibles dans notre contexte. Nous avons étudié l'application de réseaux génératifs ainsi que de méthodes modélisation 3D à notre problématique. Nous nous sommes appuyés sur ces données pour proposer différentes stratégies d'entraînement et deux nouvelles architectures. L'approche par fusion proposée associée aux stratégies d'entraînement permet de tirer profit de jeux de données génériques et de jeux de données spécifiques, afin d'améliorer les capacités de généralisation des méthodes d'estimation de poses à l'intérieur d'une voiture, en particulier sur le bas du corps
Research into autonomous cars has made great strides in recent decades, focusing particularly on analysis of the external environment and driving-related tasks. This has led to a significant increase in the autonomy of private vehicles. In this new context, it may be relevant to take an interest in the passengers of these autonomous vehicles, to study their behavior in the face of this revolution in the means of transport. The AURA AutoBehave project has been set up to explore these issues in greater depth. This project brings together several laboratories conducting research in different scientific disciplines linked to this theme, such as computer vision, biomechanics, emotions, and transport economics. This thesis carried out at the LIRIS laboratory is part of this project, in which we focus on methods for estimating the human poses of passengers using deep learning. We first looked at state-of-the-art solutions and developed both a dataset and a metric better suited to the constraints of our context. We also studied the visibility of the keypoints to help estimate the pose. We then tackled the problem of domain generalisation for pose estimation to propose an efficient solution under unknown conditions. Thus, we focused on the generation of synthetic passenger data for pose estimation. Among other things, we studied the application of generative networks and 3D modeling methods to our problem. We have used this data to propose different training strategies and two new network architectures. The proposed fusion approach associated with the training strategies makes it possible to take advantage of both generic and specific datasets, to improve the generalisation capabilities of pose estimation methods inside a car, particularly on the lower body

Books on the topic "Domain generalisation":

1

Aubreville, Marc, David Zimmerer, and Mattias Heinrich, eds. Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3.

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Aubreville, Marc, David Zimmerer, and Mattias Heinrich. Biomedical Image Registration, Domain Generalisation and Out-Of-Distribution Analysis: MICCAI 2021 Challenges, MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27-October 1, 2021, Proceedings. Springer International Publishing AG, 2022.

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Book chapters on the topic "Domain generalisation":

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Fick, Rutger H. J., Alireza Moshayedi, Gauthier Roy, Jules Dedieu, Stéphanie Petit, and Saima Ben Hadj. "Domain-Specific Cycle-GAN Augmentation Improves Domain Generalizability for Mitosis Detection." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 40–47. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_5.

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Long, Xi, Ying Cheng, Xiao Mu, Lian Liu, and Jingxin Liu. "Domain Adaptive Cascade R-CNN for MItosis DOmain Generalization (MIDOG) Challenge." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 73–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_11.

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Almahfouz Nasser, Sahar, Nikhil Cherian Kurian, and Amit Sethi. "Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 77–80. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_12.

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Wilm, Frauke, Christian Marzahl, Katharina Breininger, and Marc Aubreville. "Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization Challenge." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 5–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_1.

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Chung, Youjin, Jihoon Cho, and Jinah Park. "Domain-Robust Mitotic Figure Detection with Style Transfer." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 23–31. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_3.

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Yang, Sen, Feng Luo, Jun Zhang, and Xiyue Wang. "Sk-Unet Model with Fourier Domain for Mitosis Detection." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 86–90. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_14.

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Jahanifar, Mostafa, Adam Shepard, Neda Zamanitajeddin, R. M. Saad Bashir, Mohsin Bilal, Syed Ali Khurram, Fayyaz Minhas, and Nasir Rajpoot. "Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 48–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_6.

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Nateghi, Ramin, and Fattaneh Pourakpour. "Two-Step Domain Adaptation for Mitotic Cell Detection in Histopathology Images." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 32–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_4.

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Lafarge, Maxime W., and Viktor H. Koelzer. "Rotation Invariance and Extensive Data Augmentation: A Strategy for the MItosis DOmain Generalization (MIDOG) Challenge." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 62–67. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_9.

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Tan, Jeremy, Turkay Kart, Benjamin Hou, James Batten, and Bernhard Kainz. "MetaDetector: Detecting Outliers by Learning to Learn from Self-supervision." In Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis, 119–26. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97281-3_18.

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Conference papers on the topic "Domain generalisation":

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Nguyen, Toan, Kien Do, Bao Duong, and Thin Nguyen. "Domain Generalisation via Risk Distribution Matching." In 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2024. http://dx.doi.org/10.1109/wacv57701.2024.00277.

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Le, Hoang Son, Rini Akmeliawati, and Gustavo Carneiro. "Combining Data Augmentation and Domain Distance Minimisation to Reduce Domain Generalisation Error." In 2021 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2021. http://dx.doi.org/10.1109/dicta52665.2021.9647203.

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Vu, Thuy-Trang, Shahram Khadivi, Dinh Phung, and Gholamreza Haffari. "Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training." In Findings of the Association for Computational Linguistics: ACL 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-acl.49.

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Arsenos, Anastasios, Dimitrios Kollias, Evangelos Petrongonas, Christos Skliros, and Stefanos Kollias. "Uncertainty-Guided Contrastive Learning For Single Source Domain Generalisation." In ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. http://dx.doi.org/10.1109/icassp48485.2024.10448096.

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Ahmad, Rehan, Md Asif Jalal, Muhammad Umar Farooq, Anna Ollerenshaw, and Thomas Hain. "Towards Domain Generalisation in ASR with Elitist Sampling and Ensemble Knowledge Distillation." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095746.

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Chen, Qi, Bing Xue, and Mengjie Zhang. "Generalisation and domain adaptation in GP with gradient descent for symbolic regression." In 2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015. http://dx.doi.org/10.1109/cec.2015.7257017.

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Arora, Aseem, Shabbirhussain Bhaisaheb, Harshit Nigam, Manasi Patwardhan, Lovekesh Vig, and Gautam Shroff. "Adapt and Decompose: Efficient Generalization of Text-to-SQL via Domain Adapted Least-To-Most Prompting." In Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.genbench-1.3.

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Kamenou, Eleni, Jesús Martínez Del Rincón, Paul Miller, and Patricia Devlin-Hill. "A Meta-learning Approach for Domain Generalisation across Visual Modalities in Vehicle Re-identification." In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2023. http://dx.doi.org/10.1109/cvprw59228.2023.00044.

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Abeysinghe, Chamath, Chris Reid, Hamid Rezatofighi, and Bernd Meyer. "Tracking Different Ant Species: An Unsupervised Domain Adaptation Framework and a Dataset for Multi-object Tracking." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/61.

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Tracking individuals is a vital part of many experiments conducted to understand collective behaviour. Ants are the paradigmatic model system for such experiments but their lack of individually distinguishing visual features and their high colony densities make it extremely difficult to perform reliable racking automatically. Additionally, the wide diversity of their species' appearances makes a generalized approach even harder. In this paper, we propose a data-driven multi-object tracker that, for the first time, employs domain adaptation to achieve the required generalisation. This approach is built upon a joint-detection-and-tracking framework that is extended by a set of domain discriminator modules integrating an adversarial training strategy in addition to the tracking loss. In addition to this novel domain-adaptive tracking framework, we present a new dataset and a benchmark for the ant tracking problem. The dataset contains 57 video sequences with full trajectory annotation, including 30k frames captured from two different ant species moving on different background patterns. It comprises 33 and 24 sequences for source and target domains, respectively. We compare our proposed framework against other domain-adaptive and non-domain-adaptive multi-object tracking baselines using this dataset and show that incorporating domain adaptation at multiple levels of the tracking pipeline yields significant improvements. The code and the dataset are available at https://github.com/chamathabeysinghe/da-tracker.
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Pascual, Rodrigo, Jean-Claude Golinval, and Mario Razeto. "A Model Updating Method Based on the Concept of Frequency Shift Between Analytical and Experimental FRFs." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4146.

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Abstract Up to now, model updating methods based on forced responses and local matrix sensitivities use both experimental and analytical quantities (FRFs and model impedance matrices) all evaluated at the same frequency(ies). However, this approach does not consider that between a reference system (the experimental model) and a perturbed one (the initial finite elements model), it exists a shift along the frequency axis that can be estimated using the Frequency Domain Assurance Criterion. In this paper, a model updating method based on this observation is introduced. It can be interpreted as a generalisation of the existent techniques.

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