74 citations · 83 across the 6 of their papers we have counts for
6 papers
MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition
Chuanguang Yang, Zhulin An, Helong Zhou +5
Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perfor…
Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution
Chuanguang Yang, Zhulin An, Linhang Cai +1
Knowledge distillation (KD) is an effective framework that aims to transfer meaningful information from a large teacher to a smaller student. Generally, KD often involves how to de…
Hierarchical Self-supervised Augmented Knowledge Distillation
Chuanguang Yang, Zhulin An, Linhang Cai +1
Knowledge distillation often involves how to define and transfer knowledge from teacher to student effectively. Although recent self-supervised contrastive knowledge achieves the b…
Mutual Contrastive Learning for Visual Representation Learning
Chuanguang Yang, Zhulin An, Linhang Cai +1
We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interactio…
GHFP: Gradually Hard Filter Pruning
Linhang Cai, Zhulin An, Yongjun Xu
Filter pruning is widely used to reduce the computation of deep learning, enabling the deployment of Deep Neural Networks (DNNs) in resource-limited devices. Conventional Hard Filt…
Softer Pruning, Incremental Regularization
Linhang Cai, Zhulin An, Chuanguang Yang +1
Network pruning is widely used to compress Deep Neural Networks (DNNs). The Soft Filter Pruning (SFP) method zeroizes the pruned filters during training while updating them in the…