Controllable Invariance through Adversarial Feature Learning

TL;DR

This paper introduces an adversarial learning framework to develop invariant data representations, improving generalization across tasks like bias reduction, language independence, and lighting invariance.

Contribution

It formulates the invariant representation learning as an adversarial minimax game and analyzes its optimal equilibrium, advancing understanding of invariant feature learning.

Findings

Achieves invariant representations in three benchmark tasks.

Improves generalization performance in bias-free classification.

Demonstrates effectiveness in language and lighting invariance.

Abstract

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. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance.