Neural Networks for Learning Counterfactual G-Invariances from Single Environments
This addresses the challenge of improving neural network generalization for transformation groups, which is incremental as it builds on existing invariance-driven methods but allows single-environment extrapolation.
The paper tackles the problem of neural networks' inability to extrapolate beyond training data for group transformations, showing that this limitation stems from underspecified outcomes in learning hypotheses rather than model capacity, and introduces a counterfactual learning framework that enables extrapolation from a single environment, validated on sequence and image tasks.
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.