451 citations · 1.1k across the 28 of their papers we have counts for
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ERM++: An Improved Baseline for Domain Generalization
Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis +2
Domain Generalization (DG) aims to develop classifiers that can generalize to new, unseen data distributions, a critical capability when collecting new domain-specific data is impr…
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Dina Bashkirova, Dan Hendrycks, Donghyun Kim +5
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i.e., the same domain. This over-estimates future accuracy on o…
TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation
Toshihiko Matsuura, Kuniaki Saito, Tatsuya Harada
The task of unsupervised domain adaptation is proposed to transfer the knowledge of a label-rich domain (source domain) to a label-scarce domain (target domain). Matching feature d…
DeMIAN: Deep Modality Invariant Adversarial Network
Kuniaki Saito, Yusuke Mukuta, Yoshitaka Ushiku +1
Obtaining common representations from different modalities is important in that they are interchangeable with each other in a classification problem. For example, we can train a cl…