2022-07-26 更新
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
Authors:Chuanguang Yang, Zhulin An, Helong Zhou, Yongjun Xu, Qian Zhan
The teacher-free online Knowledge Distillation (KD) aims to train an ensemble of multiple student models collaboratively and distill knowledge from each other. Although existing online KD methods achieve desirable performance, they often focus on class probabilities as the core knowledge type, ignoring the valuable feature representational information. We present a Mutual Contrastive Learning (MCL) framework for online KD. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributions among a cohort of networks in an online manner. Our MCL can aggregate cross-network embedding information and maximize the lower bound to the mutual information between two networks. This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks. Beyond the final layer, we extend MCL to several intermediate layers assisted by auxiliary feature refinement modules. This further enhances the ability of representation learning for online KD. Experiments on image classification and transfer learning to visual recognition tasks show that MCL can lead to consistent performance gains against state-of-the-art online KD approaches. The superiority demonstrates that MCL can guide the network to generate better feature representations. Our code is publicly available at https://github.com/winycg/MCL.
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Contrastive Monotonic Pixel-Level Modulation
Authors:Kun Lu, Rongpeng Li, Honggang Zhang
Continuous one-to-many mapping is a less investigated yet important task in both low-level visions and neural image translation. In this paper, we present a new formulation called MonoPix, an unsupervised and contrastive continuous modulation model, and take a step further to enable a pixel-level spatial control which is critical but can not be properly handled previously. The key feature of this work is to model the monotonicity between controlling signals and the domain discriminator with a novel contrastive modulation framework and corresponding monotonicity constraints. We have also introduced a selective inference strategy with logarithmic approximation complexity and support fast domain adaptations. The state-of-the-art performance is validated on a variety of continuous mapping tasks, including AFHQ cat-dog and Yosemite summer-winter translation. The introduced approach also helps to provide a new solution for many low-level tasks like low-light enhancement and natural noise generation, which is beyond the long-established practice of one-to-one training and inference. Code is available at https://github.com/lukun199/MonoPix.
PDF ECCV’2022 Oral presentation, including both main paper and supp
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