Alignment with human representations supports robust few-shot learning

• URL: http://arxiv.org/abs/2301.11990v3
• Date: Sun, 29 Oct 2023 19:45:09 GMT
• Title: Alignment with human representations supports robust few-shot learning
• Authors: Ilia Sucholutsky, Thomas L. Griffiths
• Abstract summary: We show there should be a U-shaped relationship between the degree of representational alignment with humans and performance on few-shot learning tasks. We also show that highly-aligned models are more robust to both natural adversarial attacks and domain shifts. Our results suggest that human-alignment is often a sufficient, but not necessary, condition for models to make effective use of limited data, be robust, and generalize well.
• Score: 14.918671859247429
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Should we care whether AI systems have representations of the world that are similar to those of humans? We provide an information-theoretic analysis that suggests that there should be a U-shaped relationship between the degree of representational alignment with humans and performance on few-shot learning tasks. We confirm this prediction empirically, finding such a relationship in an analysis of the performance of 491 computer vision models. We also show that highly-aligned models are more robust to both natural adversarial attacks and domain shifts. Our results suggest that human-alignment is often a sufficient, but not necessary, condition for models to make effective use of limited data, be robust, and generalize well.

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