arXiv AI
Can One-Shot Test-Time Data Augmentation Help with Generalization?
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Efficient Dataset Distillation for Pre-Trained Self-Supervised Models via Statistical Flow Matching
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Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
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Interleaved Noise Injection Improves Clean, Corrupted, and OOD Performance
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arXiv Machine Learning
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Fast Equivariant Imaging: Accelerating Unsupervised Learning and Model Adaptation via Inexact Splitting
arXiv:2507. 06764v5 Announce Type: replace-cross Abstract: In this work, we propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to rapidly and efficiently train deep imaging networks without ground-truth data.