24 citations · 49 across the 12 of their papers we have counts for
7 papers · 1 filter
Geometric Regularity in Deterministic Sampling Dynamics of Diffusion-based Generative Models
Defang Chen, Zhenyu Zhou, Can Wang +1
Diffusion-based generative models employ stochastic differential equations (SDEs) and their equivalent probability flow ordinary differential equations (ODEs) to establish a smooth…
On the Trajectory Regularity of ODE-based Diffusion Sampling
Defang Chen, Zhenyu Zhou, Can Wang +2
Diffusion-based generative models use stochastic differential equations (SDEs) and their equivalent ordinary differential equations (ODEs) to establish a smooth connection between…
Knowledge Translation: A New Pathway for Model Compression
Wujie Sun, Defang Chen, Jiawei Chen +3
Deep learning has witnessed significant advancements in recent years at the cost of increasing training, inference, and model storage overhead. While existing model compression met…
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…
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…
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…