8 citations · 11 across the 8 of their papers we have counts for
5 papers · 1 filter
Exploring Dark Knowledge under Various Teacher Capacities and Addressing Capacity Mismatch
Wen-Shu Fan, Xin-Chun Li, De-Chuan Zhan
Knowledge Distillation (KD) could transfer the ``dark knowledge" of a well-performed yet large neural network to a weaker but lightweight one. From the view of output logits and so…
Visualizing, Rethinking, and Mining the Loss Landscape of Deep Neural Networks
Yichu Xu, Xin-Chun Li, Lan Li +1
The loss landscape of deep neural networks (DNNs) is commonly considered complex and wildly fluctuated. However, an interesting observation is that the loss surfaces plotted along…
Exploring and Exploiting the Asymmetric Valley of Deep Neural Networks
Xin-Chun Li, Jin-Lin Tang, Bo Zhang +2
Exploring the loss landscape offers insights into the inherent principles of deep neural networks (DNNs). Recent work suggests an additional asymmetry of the valley beyond the flat…
MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
Xin-Chun Li, Shaoming Song, Yinchuan Li +4
In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients wi…
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
Xin-Chun Li, Le Gan, De-Chuan Zhan +3
Although federated learning (FL) has recently been proposed for efficient distributed training and data privacy protection, it still encounters many obstacles. One of these is the…