24 citations · 48 across the 8 of their papers we have counts for
6 papers · 1 filter
Simple and Fast Distillation of Diffusion Models
Zhenyu Zhou, Defang Chen, Can Wang +2
Diffusion-based generative models have demonstrated their powerful performance across various tasks, but this comes at a cost of the slow sampling speed. To achieve both efficient…
Conditional Image Synthesis with Diffusion Models: A Survey
Zheyuan Zhan, Defang Chen, Jian-Ping Mei +5
Conditional image synthesis based on user-specified requirements is a key component in creating complex visual content. In recent years, diffusion-based generative modeling has bec…
Knowledge Distillation with the Reused Teacher Classifier
Defang Chen, Jian-Ping Mei, Hailin Zhang +3
Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance. For this purpose, various ap…
Distilling Holistic Knowledge with Graph Neural Networks
Sheng Zhou, Yucheng Wang, Defang Chen +4
Knowledge Distillation (KD) aims at transferring knowledge from a larger well-optimized teacher network to a smaller learnable student network.Existing KD methods have mainly consi…
Cross-Layer Distillation with Semantic Calibration
Defang Chen, Jian-Ping Mei, Yuan Zhang +3
Knowledge distillation is a technique to enhance the generalization ability of a student model by exploiting outputs from a teacher model. Recently, feature-map based variants expl…
Online Knowledge Distillation via Multi-branch Diversity Enhancement
Zheng Li, Ying Huang, Defang Chen +3
Knowledge distillation is an effective method to transfer the knowledge from the cumbersome teacher model to the lightweight student model. Online knowledge distillation uses the e…