Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation

• URL: http://arxiv.org/abs/2205.14141v1
• Date: Fri, 27 May 2022 17:59:36 GMT
• Title: Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation
• Authors: Yixuan Wei and Han Hu and Zhenda Xie and Zheng Zhang and Yue Cao and Jianmin Bao and Dong Chen and Baining Guo
• Abstract summary: Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances. In this paper, we show that the inferior fine-tuning performance of pre-training approaches can be significantly improved by a simple post-processing.
• Score: 42.37533586611174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Masked image modeling (MIM) learns representations with remarkably good fine-tuning performances, overshadowing previous prevalent pre-training approaches such as image classification, instance contrastive learning, and image-text alignment. In this paper, we show that the inferior fine-tuning performance of these pre-training approaches can be significantly improved by a simple post-processing in the form of feature distillation (FD). The feature distillation converts the old representations to new representations that have a few desirable properties just like those representations produced by MIM. These properties, which we aggregately refer to as optimization friendliness, are identified and analyzed by a set of attention- and optimization-related diagnosis tools. With these properties, the new representations show strong fine-tuning performance. Specifically, the contrastive self-supervised learning methods are made as competitive in fine-tuning as the state-of-the-art masked image modeling (MIM) algorithms. The CLIP models' fine-tuning performance is also significantly improved, with a CLIP ViT-L model reaching 89.0% top-1 accuracy on ImageNet-1K classification. More importantly, our work provides a way for the future research to focus more effort on the generality and scalability of the learnt representations without being pre-occupied with optimization friendliness since it can be enhanced rather easily. The code will be available at https://github.com/SwinTransformer/Feature-Distillation.

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