PEER Pressure: Model-to-Model Regularization for Single Source Domain Generalization
Keywords: Domain Generalization, Representation Learning, Data Augmentation
TL;DR: We reveal an overlooked issue of augmentation-based generalization approaches, and suggest a simple model-to-model regularization method to address it.
Abstract: Neural networks are frequently deployed on multiple unseen target domains, which are distributionally different from the source domain on which the model is trained. Data augmentation is the most popular tool for single source domain generalization, which expands the source domain by generating simulated ones, commonly adopted by existing approaches. In this work, we observe that the performance of such augmentation-based methods in the target domains frequently fluctuates during training, posing challenges in model selection under realistic scenarios. We argue that the fluctuation stems from the inability of the model to accumulate the knowledge learned from diverse augmentations, exacerbating feature distortion during training. Based on this observation, we propose a novel generalization method, coined Parameter-Space Ensemble with Entropy Regularization (PEER), that uses a proxy model to learn the augmented data on behalf of the main model. The main model is updated by averaging its parameters with the proxy model, progressively accumulating knowledge over the training steps. Maximizing the mutual information between the output representations of the two models guides the learning process of the proxy model, mitigating feature distortion during training. Extensive experimental results demonstrate the effectiveness of PEER in reducing the OOD performance fluctuation and enhancing generalization across various datasets, including PACS, Digits, Office-Home, and VLCS. Notably, our method with simple random augmentation achieves state-of-the-art performance, surpassing prior approaches on sDG that utilize complex data augmentation strategies.
Supplementary Material: zip
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 5175
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