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Disentangled and invariant representations are two critical goals ofrepresentation learning and many approaches have been proposed to achieveeither one of them. However, those two goals are actually complementary to eachother so that we propose a framework to accomplish both of them simultaneously.We introduce a weakly supervised signal to learn disentangled representationwhich consists of three splits containing predictive, known nuisance andunknown nuisance information respectively. Furthermore, we incorporatecontrastive method to enforce representation invariance. Experiments shows thatthe proposed method outperforms state-of-the-art (SOTA) methods on fourstandard benchmarks and shows that the proposed method can have betteradversarial defense ability comparing to other methods without adversarialtraining.

Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors