Controllable Invariance through Adversarial Feature Learning

Understand

Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning.

• In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data.
• The representation learning process is formulated as an adversarial minimax game.
• We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions.