Class Is Invariant to Context and Vice Versa: On Learning Invariance for Out-Of-Distribution Generalization
This addresses the problem of model bias in imbalanced datasets for researchers and practitioners in machine learning, offering a novel approach to improve generalization without context annotations.
The paper tackles out-of-distribution generalization by learning invariance to context without needing context labels, using a method that minimizes contrastive loss to ensure intra-class similarity is invariant across classes, achieving state-of-the-art performance on benchmarks with various biases and domain gaps.
Out-Of-Distribution generalization (OOD) is all about learning invariance against environmental changes. If the context in every class is evenly distributed, OOD would be trivial because the context can be easily removed due to an underlying principle: class is invariant to context. However, collecting such a balanced dataset is impractical. Learning on imbalanced data makes the model bias to context and thus hurts OOD. Therefore, the key to OOD is context balance. We argue that the widely adopted assumption in prior work, the context bias can be directly annotated or estimated from biased class prediction, renders the context incomplete or even incorrect. In contrast, we point out the everoverlooked other side of the above principle: context is also invariant to class, which motivates us to consider the classes (which are already labeled) as the varying environments to resolve context bias (without context labels). We implement this idea by minimizing the contrastive loss of intra-class sample similarity while assuring this similarity to be invariant across all classes. On benchmarks with various context biases and domain gaps, we show that a simple re-weighting based classifier equipped with our context estimation achieves state-of-the-art performance. We provide the theoretical justifications in Appendix and codes on https://github.com/simpleshinobu/IRMCon.