Learning explanations that are hard to varyDownload PDF

28 Sept 2020, 15:50 (edited 10 Feb 2022)ICLR 2021 PosterReaders: Everyone
  • Keywords: invariances, consistency, gradient alignment
  • 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.
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  • Code: [![github](/images/github_icon.svg) pytorch/ignite](https://github.com/pytorch/ignite) + [![Papers with Code](/images/pwc_icon.svg) 2 community implementations](https://paperswithcode.com/paper/?openreview=hb1sDDSLbV)
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