Multi-view Domain Generalization for Visual Recognition

By Li Niu, Wen Li, Dong Xu. 2015

65 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Ophthalmology

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Citations in the corpus

Learning Robust Global Representations by Penalizing Local Predictive Power (2019, background): “In domain generalization (DG) methods (Muandet et al., 2013) (or sometimes known as “zero shot domain adaptation” (Kumagai and Iwata, 2018; Niu et al., 2015; Erfani et al., 2016; Li et al., 2017c)) one possesses domain identiﬁers for a number of known in-sample domains, and the goal is to…”
Learning Robust Representations by Projecting Superficial Statistics Out (2018, methodology): “…2013) is relatively new, but has also been studied extensively: covering a wide spectrum of techniques from kernel methods (Muandet et al., 2013; Niu et al., 2015; Erfani et al., 2016; Li et al., 2017c) to more recent deep learning end-to-end methods, where the methods mostly fall into two…”
Unified Deep Supervised Domain Adaptation and Generalization (2017, background): “The second approach to DG is to exploit all information from the training domains to train a classifier or regulate its weights [32, 17, 65, 45, 46].”
Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-Grained Classification (2018, background): “There also exist domain generalization approaches [50, 27, 30] when the target domain is unseen in the training stage.”
DoveNet: Deep Image Harmonization via Domain Verification (2019, methodology): “Following the terminology in domain adaptation [32, 29] and domain generalization [31, 30], we refer to each capture condition as one domain and there could be numerous possible domains.”
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs (2018, background): “[27] proposed an approach for multi-view domain generalization.”
Deep Stable Learning for Out-Of-Distribution Generalization (2021, background): “Some pioneering methods require clear and significant heterogeneity, namely that the domains are manually divided and labeled [61, 16, 46, 9, 42], which cannot be always satisfied in real applications.”
DLOW: Domain Flow for Adaptation and Generalization (2018, background): “Domain generalization is a similar problem, which aims to learn a model that could be generalized to an unseen target domain by using multiple labeled source domains [42, 15, 45, 41, 44, 34, 36, 35].”
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (2020, background): “…seen domains (Blanchard et al., 2011; Li et al., 2018b; Muandet et al., 2013), improving the classiﬁers by fusing classiﬁers learned from seen domains (Niu et al., 2015a;b), and decomposing the classiﬁers into domain-speciﬁc and domain-invariant components (Khosla et al., 2012; Li et al., 2017a).”