24 citations · 65 across the 29 of their papers we have counts for
8 papers · 1 filter
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Wujie Sun, Defang Chen, Can Wang +3
Although diffusion model has shown great potential for generating higher quality images than GANs, slow sampling speed hinders its wide application in practice. Progressive distill…
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision
Jiongyu Guo, Defang Chen, Can Wang
Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their strong ability in handling irregular graph data…
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization
Junkun Yuan, Xu Ma, Defang Chen +3
Considerable progress has been made in domain generalization (DG) which aims to learn a generalizable model from multiple well-annotated source domains to unknown target domains. H…
Confidence-aware Self-Semantic Distillation on Knowledge Graph Embedding
Yichen Liu, Jiawei Chen, Defang Chen +3
Knowledge Graph Embedding (KGE), which projects entities and relations into continuous vector spaces, has garnered significant attention. Although high-dimensional KGE methods offe…
Alignahead: Online Cross-Layer Knowledge Extraction on Graph Neural Networks
Jiongyu Guo, Defang Chen, Can Wang
Existing knowledge distillation methods on graph neural networks (GNNs) are almost offline, where the student model extracts knowledge from a powerful teacher model to improve its…
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…
Confidence-Aware Multi-Teacher Knowledge Distillation
Hailin Zhang, Defang Chen, Can Wang
Knowledge distillation is initially introduced to utilize additional supervision from a single teacher model for the student model training. To boost the student performance, some…
Knowledge Distillation with Deep Supervision
Shiya Luo, Defang Chen, Can Wang
Knowledge distillation aims to enhance the performance of a lightweight student model by exploiting the knowledge from a pre-trained cumbersome teacher model. However, in the tradi…