On genuine invariance learning without weight-tying

TL;DR

This paper examines how neural networks learn invariance from data versus genuine invariance from weight-tying, revealing limitations and proposing regularization methods to achieve true invariance that is robust to input shifts.

Contribution

It introduces metrics to quantify learned invariance and demonstrates regularization techniques that promote genuine invariance comparable to weight-tying models.

Findings

Learned invariance is data-dependent and unreliable under distribution shifts.

Regularization can guide networks to achieve genuine invariance similar to weight-tying.

Spectral decay phenomenon reveals how networks reduce sensitivity to specific transformations.

Abstract

In this paper, we investigate properties and limitations of invariance learned by neural networks from the data compared to the genuine invariance achieved through invariant weight-tying. To do so, we adopt a group theoretical perspective and analyze invariance learning in neural networks without weight-tying constraints. We demonstrate that even when a network learns to correctly classify samples on a group orbit, the underlying decision-making in such a model does not attain genuine invariance. Instead, learned invariance is strongly conditioned on the input data, rendering it unreliable if the input distribution shifts. We next demonstrate how to guide invariance learning toward genuine invariance by regularizing the invariance of a model at the training. To this end, we propose several metrics to quantify learned invariance: (i) predictive distribution invariance, (ii) logit invariance, and (iii) saliency invariance similarity. We show that the invariance learned with the invariance error regularization closely reassembles the genuine invariance of weight-tying models and reliably holds even under a severe input distribution shift. Closer analysis of the learned invariance also reveals the spectral decay phenomenon, when a network chooses to achieve the invariance to a specific transformation group by reducing the sensitivity to any input perturbation.