Learning explanations that are hard to vary

Code

• github.com/pytorch/ignite
  OfficialIn paperpytorch★ 4,746
• github.com/gibipara92/learning-explanations-hard-to-vary
  OfficialIn paperpytorch★ 41
• github.com/facebookresearch/DomainBed
  pytorch★ 1,604
• github.com/jefferythewind/era-splitting-notebook-examples
  none★ 6

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.

Tasks

Reproductions