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Domain Generalization: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI ...
Kaiyang Zhou +4 more
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Generalizing to Unseen Domains: A Survey on Domain Generalization
IEEE Transactions on Knowledge and Data Engineering, 2022 Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years.
Wang, Jindong +8 more
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Generalized score matching for general domains [PDF]
Information and Inference: A Journal of the IMA, 2021 Abstract Estimation of density functions supported on general domains arises when the data are naturally restricted to a proper subset of the real space. This problem is complicated by typically intractable normalizing constants. Score matching provides a powerful tool for estimating densities with such intractable normalizing constants ...
Yu, Shiqing +2 more
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Heterogeneous Domain Generalization Via Domain Mixup [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain generalization which aims to improve the generalization capability across different tasks, which is, how to learn a DCNN model with multiple domain data such that the trained ...
Wang, Yufei, Li, Haoliang, Kot, Alex C.
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Inter-domain curriculum learning for domain generalization
ICT Express, 2022 Domain generalization aims to learn a domain-invariant representation from multiple source domains so that a model can generalize well across unseen target domains.
Daehee Kim, Jinkyu Kim, Jaekoo Lee
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Pivot-Guided Embedding for Domain Generalization
IEEE Access, 2022 Neural networks have suffered from a distribution gap between training and test data, known as domain shift. Domain generalization (DG) methods aim to learn domain invariant representations only with limited source domain data to cope with unseen target ...
Hyun Seok Seong +3 more
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Domain Generalization Model of Deep Convolutional Networks Based on SAND-Mask
Algorithms, 2022 In the actual operation of the machine, due to a large number of operating conditions and a wide range of operating conditions, the data under many operating conditions cannot be obtained.
Jigang Wang, Liang Chen, Rui Wang
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Adversarial Reconstruction Loss for Domain Generalization
IEEE Access, 2021 The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not ...
Imad Eddine Ibrahim Bekkouch +5 more
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Learning to Generate Novel Domains for Domain Generalization [PDF]
, 2020 This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains. A main challenge for DG is that the available source domains often exhibit limited diversity, hampering the model's ability to learn to generalize.
Zhou, Kaiyang +3 more
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Generalizing to Unseen Domains: A Survey on Domain Generalization [PDF]
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021 Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increased interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain.
Jindong Wang +4 more
openaire +1 more source