Numerical experiments show that our algorithmic framework has achieved superior and stable performance in various datasets, such as Colored MNIST and Punctuated ...
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Apr 1, 2021 · We present a new adversarial training method, Domain Invariant Adversarial Learning (DIAL), which learns a feature representation that is both ...
Numerical experiments show that our algorithmic framework has achieved superior and stable performance in various datasets, such as Colored MNIST and Punctuated ...
We provide the detail of selected model in one of the random trials for each of the ColoredMNIST and PSST dataset. They are located in the *-Sample-Output ...
Apr 29, 2019 · To overcome the problem, this study first expands the analysis of the trade-off by Xie et. al., and provides the notion of accuracy-constrained ...
In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is ...
In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is ...
Abstract. Learning domain-invariant representation is a dominant ap- proach for domain generalization (DG), where we need to build a classifier.
Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by ...
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