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cs.CV2024
Feature Augmentation for Self-supervised Contrastive Learning: A Closer Look
Yong Zhang, Rui Zhu, Shifeng Zhang +3
Self-supervised contrastive learning heavily relies on the view variance brought by data augmentation, so that it can learn a view-invariant pre-trained representation. Beyond incr…
cs.CV2024★ 1 cited
SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer
Rui Zhu, Yingwei Pan, Yehao Li +4
Diffusion Transformer (DiT) has emerged as the new trend of generative diffusion models on image generation. In view of extremely slow convergence in typical DiT, recent breakthrou…