Diffusion-Based Data Augmentation for Unsupervised Contrastive Learning
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Detailed research presentation on novel diffusion-based data augmentation methods for unsupervised contrastive learning. Introduced the DiffAug framework that leverages diffusion models to generate semantically consistent augmented views from scratch, addressing the limitations of traditional augmentation techniques. Demonstrated superior performance on image representation learning tasks, showing significant improvements in downstream classification, detection, and segmentation tasks. This work was later published at ICML 2024.