Learning explanations that are hard to vary

TL;DR

This paper explores the principle that effective explanations in deep learning are those that are hard to vary, proposing a new method that emphasizes invariances over memorization to improve model explanations.

Contribution

It formalizes a notion of loss surface minima consistency and introduces an alternative algorithm based on a logical AND to focus on invariances, reducing memorization.

Findings

Averaging gradients can lead to memorization and patchwork solutions.

The proposed AND-based algorithm enhances invariance detection.

Experimental validation shows improved focus on invariant features.

Abstract

In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning. We show that averaging gradients across examples -- akin to a logical OR of patterns -- can favor memorization and `patchwork' solutions that sew together different strategies, instead of identifying invariances. To inspect this, we first formalize a notion of consistency for minima of the loss surface, which measures to what extent a minimum appears only when examples are pooled. We then propose and experimentally validate a simple alternative algorithm based on a logical AND, that focuses on invariances and prevents memorization in a set of real-world tasks. Finally, using a synthetic dataset with a clear distinction between invariant and spurious mechanisms, we dissect learning signals and compare this approach to well-established regularizers.