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Robustness and Adaptation to Hidden Factors of Variation

arXiv 2203.01864 ↗First posted 3 Mar 2022cs.LG cs.CVcited by 0

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Abstract (arXiv)

We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of variations in the data. Towards this end, we employ a two step strategy that a) does unsupervised discovery, via generative models, of sensitive factors that cause models to under-perform, and b) intervenes models to make their performance invariant to these sensitive factors' influence. We consider 3 separate interventions for robustness, including: data augmentation, semantic consistency, and adversarial alignment. We evaluate our method using metrics that measure trade offs between invariance (insensitivity) and overall performance (utility) and show the benefits of our method for 3 settings (unsupervised, semi-supervised and generalization).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 31 references that are in the map. First posted on arXiv 3 Mar 2022.

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25 of 31 references.

References · 25

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13 more
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