When and How to Canonize: A Generalization Perspective
While invariant architectures are standard for processing symmetric data, there is growing interest in achieving invariance by applying group averaging or canonization to non-invariant backbones. However, the theoretical generalization properties of these alternative strategies remain poorly understood. We introduce a theoretical framework to analyze the generalization error of these methods by bounding their covering numbers. We establish a rigorous generalization hierarchy: the error bounds of canonized models are at best equal to the error bounds of structurally invariant and group-averaged models, and at worst equal to the bounds of non-invariant baselines. Furthermore, we show that t...
| Authors | Yonatan Sverdlov, Benjamin Friedman, Snir Hordan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 May 2026 |
| Last updated at source | 11 October 2026 |
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