Zero-shot generalization across architectures for visual classification

Evan Gerritz, Luciano Dyballa, Steven W. Zucker

Keywords: zero-shot learning, fine-tuning, image classification, deep neural networks, vision transformers

TL;DR: Popular networks, from deep convolutional networks (CNNs) to transformers, vary in their power to extrapolate to unseen classes both across layers and across architectures.

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.

Submission Number: 214