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Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning

12 Feb 2021 • Yifan Zhang • Bryan Hooi • Dapeng Hu • Jian Liang • Jiashi Feng

Contrastive self-supervised learning (CSL) leverages unlabeled data to train models that provide instance-discriminative visual representations uniformly scattered in the feature space. In deployment, the common practice is to directly fine-tune models with the cross-entropy loss, which however may not be an optimal strategy... (read more)

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METHOD TYPE
Supervised Contrastive Loss
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Mixup
Image Data Augmentation
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