Exploiting Low-Rank Structure from Latent Domains for Domain Generalization

By Zheng Xu, Wen Li, Li Niu, Dong Xu. 2014

204 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Multiple-Source Adaptation with Domain Classifiers (2020, background): “…and unlabeled data available from the target domain [Mansour et al., 2008, 2009a, Hoffman et al., 2018, Pan and Yang, 2010, Muandet et al., 2013, Xu et al., 2014, Hoffman et al., 2012, Gong et al., 2013a,b, Zhang et al., 2015, Ganin et al., 2016, Tzeng et al., 2015, Motiian et al., 2017b,a,…”
Agnostic Federated Learning (2019, background): “The problem of domain generalization (Pan and Yang, 2010; Muandet et al., 2013; Xu et al., 2014), where knowledge from an arbitrary number of related domains is combined to perform well on a previously unseen domain is very closely related to that of federated learning, though the assumptions about…”
Domain Generalization via Conditional Invariant Representations (2018, methodology): “Domain generalization has been widely applied in classi-ﬁcation tasks (Xu et al. 2014; Duan et al. 2009; Muan-det, Balduzzi, and Sch ¨ olkopf 2013; Ghifary et al. 2017; Ghifary et al. 2015; Erfani et al. 2016).”
Deeper, Broader and Artier Domain Generalization (2017, neutral): “Another intuition is to predict which known domain a testing sample seems most relevant to, and use that classifier [30].”
Domain Generalization for Object Recognition with Multi-task Autoencoders (2015, methodology): “On average, D-MTAE+1HNN has better performance than the prior state-of-the-art on this dataset, LRE-SVM [38].”
Unified Deep Supervised Domain Adaptation and Generalization (2017, methodology): “1NN SVM LB UML [17] LRE-SVM [65] SCA [23] CCSA”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “is to explore the unlabeled source data and to discover repetitive patterns that could be used for the supervised learning task. On the other hand, DA is more closely related to domain generalization [221, 222, 138, 223, 224], multitask learning [225, 226, 227] or few-shot learning [228, 229] discus”
A Deeper Look at Dataset Bias (2015, methodology): “Several domain adaptive methods have been tested on setups extracted from multiple datasets [12,16] and some approaches proposed to identify the latent domains in the data collections before applying domain adaptation solutions [22,14,43].”
Domain Generalization with Adversarial Feature Learning (2018, methodology): “• LRE-SVM [39]: We train an exemplar-SVM with a low-rank regularization.”
Domain Generalization: A Survey (2021, methodology): “[45] have extended exemplar-SVMs to DG.”
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization (2015, methodology): “The closest competitor to SCA is LRE-SVM.”
Structure-Preserved Multi-source Domain Adaptation (2016, methodology): “In classifier adaptation, usually a classifier learned in the source, e.g., SVM, is adapted for target data [1], [5], [34].”
Learning Attributes Equals Multi-Source Domain Generalization (2016, methodology): “To this end, we cast attribute detection as a multi-source domain generalization problem [50, 80, 51] by noting that the desired properties from attributes are analogous to the objective of the latter.”
Generalising Fine-Grained Sketch-Based Image Retrieval (2019, background): “Overall our framework can be considered as a solution to domain generalisation [26] that adapts a model via a domain-descriptor, but where the descriptor is estimated from a single data instance rather than assuming it is given as metadata [32, 33]; and where the perspective on descriptor definition”
Multi-view Domain Generalization for Visual Recognition (2015, methodology): “Meanwhile, the domain generalization techniques [30, 38, 31] were also developed to learn robust classifiers that can generalize well to any unseen target domain.”