arXiv (OAI Expanded)
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Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning
Most self-supervised learning (SSL) methods encourage invariance across augmentations, but strict flip invariance can suppress informative left--right correspondences in approximately bilateral data such as medical images and human faces. We propose Mirror-Fusion-Augmented Self-Supervised Learning (
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