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We systematically examine the necessary of each component (augmentation strategy, formulation of PaCo, the momentum
encoder and queue size) in PaCo and simplify Parametric Contrastive Learning (PaCo) to Generalized Contrastive Learning
(GPaCo) by removing the momentum encoder. GPaCo outperforms PaCo by a large margin. We verify the generality
of GPaCo on various tasks, including long-tailed recognition, recognition on full ImageNet and CIFAR-100 datasets across CNNs to vision transformers, robustness on out-of-distribution data, and semantic segmentation.
Hello!
Thanks for your excellent work! I would like to know what is the difference between GPaCo and PaCo?
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