AAAI Conference 2020 Conference Paper
Invariant Representations through Adversarial Forgetting
- Ayush Jaiswal
- Daniel Moyer
- Greg Ver Steeg
- Wael AbdAlmageed
- Premkumar Natarajan
We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism. We show that the forgetting mechanism serves as an information-bottleneck, which is manipulated by the adversarial training to learn invariance to unwanted factors. Empirical results show that the proposed framework achieves stateof-the-art performance at learning invariance in both nuisance and bias settings on a diverse collection of datasets and tasks.