Neural Networks for Learning Counterfactual G-Invariances from Single Environments

TL;DR

This paper proposes a new learning framework enabling neural networks to extrapolate invariances from single environments, addressing limitations of traditional methods that require extensive data for extrapolation.

Contribution

The authors introduce a counterfactually-guided learning framework that allows neural networks to learn invariances and perform extrapolation from a single environment, unlike existing approaches.

Findings

Framework enables extrapolation from a single environment

Sequence and image tasks validate the approach

Traditional invariance methods require more data

Abstract

Despite -- or maybe because of -- their astonishing capacity to fit data, neural networks are believed to have difficulties extrapolating beyond training data distribution. This work shows that, for extrapolations based on finite transformation groups, a model's inability to extrapolate is unrelated to its capacity. Rather, the shortcoming is inherited from a learning hypothesis: Examples not explicitly observed with infinitely many training examples have underspecified outcomes in the learner's model. In order to endow neural networks with the ability to extrapolate over group transformations, we introduce a learning framework counterfactually-guided by the learning hypothesis that any group invariance to (known) transformation groups is mandatory even without evidence, unless the learner deems it inconsistent with the training data. Unlike existing invariance-driven methods for (counterfactual) extrapolations, this framework allows extrapolations from a single environment. Finally, we introduce sequence and image extrapolation tasks that validate our framework and showcase the shortcomings of traditional approaches.