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We tackle here a specific, still not widely addressed aspect, of AIrobustness, which consists of seeking invariance / insensitivity of modelperformance to hidden factors of variations in the data. Towards this end, weemploy a two step strategy that a) does unsupervised discovery, via generativemodels, of sensitive factors that cause models to under-perform, and b)intervenes models to make their performance invariant to these sensitivefactors' 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 ourmethod for 3 settings (unsupervised, semi-supervised and generalization).

Robustness and Adaptation to Hidden Factors of Variation