Zero-shot generalization across architectures for visual classification

• URL: http://arxiv.org/abs/2402.14095v4
• Date: Fri, 3 May 2024 15:25:09 GMT
• Title: Zero-shot generalization across architectures for visual classification
• Authors: Evan Gerritz, Luciano Dyballa, Steven W. Zucker,
• Abstract summary: Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear. We show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalization to unseen data is a key desideratum for deep networks, but its relation to classification accuracy is unclear. Using a minimalist vision dataset and a measure of generalizability, we show that popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures. Accuracy is not a good predictor of generalizability, and generalization varies non-monotonically with layer depth.

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