arXiv:2505.11702v3 Announce Type: replace Abstract: This work develops a framework for post-training augmentation invariance, in which our goal is to add invariance properties to a pretrained network without altering its behavior on the original, non-augmented input distribution. We define this notion precisely and additionally introduce augmented encoders, which are probabilistic encoders that formalize augmentation-based encoding processes and that serve as our fundamental object of study. We introduce two losses for augmented encoders, namely, Markov-Wasserstein minimization and Wasserstein
This research addresses a fundamental challenge in applying AI models to real-world, varied data by improving robustness without retraining entire networks, which aligns with the increasing deployment of AI in diverse environments.
Improving the robustness and adaptability of pre-trained AI models without costly retraining could accelerate AI adoption and reduce operational overhead for businesses and researchers.
The ability to add invariance properties post-training could lead to more resilient AI systems and extend the lifespan of deployed models by making them more robust to augmented inputs.
- · AI developers
- · Companies deploying AI in varied environments
- · Machine learning researchers
- · Compute infrastructure providers (due to reduced retraining burden)
- · None
Pre-trained AI models become more robust to variations in input data without full retraining.
Reduced need for extensive data augmentation during initial training, potentially streamlining model development.
Broader and more reliable application of AI in fields requiring high adaptability to diverse real-world conditions.
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Read at arXiv cs.LG