2 citations · 2 across the 5 of their papers we have counts for
5 papers
Multi-Teacher Knowledge Distillation with Reinforcement Learning for Visual Recognition
Chuanguang Yang, Xinqiang Yu, Han Yang +4
Multi-teacher Knowledge Distillation (KD) transfers diverse knowledge from a teacher pool to a student network. The core problem of multi-teacher KD is how to balance distillation…
Online Policy Distillation with Decision-Attention
Xinqiang Yu, Chuanguang Yang, Chengqing Yu +3
Policy Distillation (PD) has become an effective method to improve deep reinforcement learning tasks. The core idea of PD is to distill policy knowledge from a teacher agent to a s…
Exemplar-Free Class Incremental Learning via Incremental Representation
Libo Huang, Zhulin An, Yan Zeng +3
Exemplar-Free Class Incremental Learning (efCIL) aims to continuously incorporate the knowledge from new classes while retaining previously learned information, without storing any…
CLIP-KD: An Empirical Study of CLIP Model Distillation
Chuanguang Yang, Zhulin An, Libo Huang +5
Contrastive Language-Image Pre-training (CLIP) has become a promising language-supervised visual pre-training framework. This paper aims to distill small CLIP models supervised by…
Categories of Response-Based, Feature-Based, and Relation-Based Knowledge Distillation
Chuanguang Yang, Xinqiang Yu, Zhulin An +1
Deep neural networks have achieved remarkable performance for artificial intelligence tasks. The success behind intelligent systems often relies on large-scale models with high com…