Post-Training Augmentation Invariance Framework Enables Pretrained Networks to Handle Image Transformations
Researchers developed a framework to add invariance properties to pretrained neural networks without altering their original performance, using lightweight adapter networks trained with novel loss functions. The method uses augmented encoders and two proposed losses—Markov-Wasserstein minimization and Wasserstein correlation maximization—to make frozen pretrained models robust to transformations like rotation and noise. This approach is significant because it enables existing models to handle augmented inputs without retraining, potentially extending the utility of deployed pretrained networks.
The paper introduces a post-training augmentation invariance framework that adds robustness to image transformations to pretrained networks while preserving their original behavior on unaugmented data. The core innovation is the use of augmented encoders—probabilistic encoders that formalize augmentation-based encoding—combined with two novel loss functions for training lightweight one-hidden-layer MLP adapter networks. These adapters are appended to the latent space of frozen pretrained models (e.g., DINO features). Empirical results demonstrate substantial improvements: on STL10, rotation invariance improved from 71% to 94% accuracy, and noise invariance from 58% to 86%, all without fine-tuning the original network weights. The adapters act nearly isometrically on non-augmented data, minimizing corruption to original features, and outperform alternative approaches like SimCLR and HSIC maximization.
What's missing
The paper does not discuss computational overhead or inference latency introduced by the adapter networks, nor does it provide extensive evaluation across diverse pretrained architectures beyond DINO or datasets beyond STL10. The generalization of the approach to other types of augmentations (e.g., color jittering, cropping) and its performance on larger-scale datasets remain open questions.
What different sources said
- arXiv cs.LGCenter
Post-Training Augmentation Invariance
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