8 citations · 21 across the 10 of their papers we have counts for
4 papers · 1 filter
Efficient Knowledge Distillation from Model Checkpoints
Chaofei Wang, Qisen Yang, Rui Huang +2
Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong…
Learning to Weight Samples for Dynamic Early-exiting Networks
Yizeng Han, Yifan Pu, Zihang Lai +6
Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks…
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
Jiayu Xiao, Liang Li, Chaofei Wang +2
Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domai…
Learn From the Past: Experience Ensemble Knowledge Distillation
Chaofei Wang, Shaowei Zhang, Shiji Song +1
Traditional knowledge distillation transfers "dark knowledge" of a pre-trained teacher network to a student network, and ignores the knowledge in the training process of the teache…