May 14, 2024, 4:46 a.m. | Nazim Bendib

cs.CV updates on

arXiv:2405.07116v1 Announce Type: new
Abstract: Data augmentation plays a critical role in generating high-quality positive and negative pairs necessary for effective contrastive learning. However, common practices involve using a single augmentation policy repeatedly to generate multiple views, potentially leading to inefficient training pairs due to a lack of cooperation between views. Furthermore, to find the optimal set of augmentations, many existing methods require extensive supervised evaluation, overlooking the evolving nature of the model that may require different augmentations throughout the …

abstract arxiv augmentation data generate however multiple negative policy positive practices quality role training type

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