To the best of our knowledge, this is one of the first to adopt differentiable environment splitting method to enable stable predictions across environments ...
We propose a novel theoretically principled min-max framework to iteratively construct a worst-case splitting, ie creating the most challenging environment ...
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Apr 1, 2021 · Abstract:The phenomenon of adversarial examples illustrates one of the most basic vulnerabilities of deep neural networks.
In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data.
Dec 18, 2022 · We propose Domainwise Adversarial Training (DAT), an AT-inspired method for alleviating distribution shift by domain-specific perturbations.
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Sep 12, 2022 · The phenomenon of adversarial examples illustrates one of the most basic vulnerabilities of deep neural networks.
We then propose a novel method adversarial feature learning with accuracy constraint (AFLAC), which explicitly leads to that invariance on adversarial training.
Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to ...
Empirical validations show that the performance of AFLAC is superior to that of domain-invariance-based methods on both synthetic and three real-world datasets, ...