250 citations · 340 across the 9 of their papers we have counts for
5 papers · 1 filter
Slimmable Generative Adversarial Networks
Liang Hou, Zehuan Yuan, Lei Huang +3
Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models makes them challenging to deploy widely in pr…
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Lei Huang, Jie Qin, Yi Zhou +3
Normalization techniques are essential for accelerating the training and improving the generalization of deep neural networks (DNNs), and have successfully been used in various app…
Group Whitening: Balancing Learning Efficiency and Representational Capacity
Lei Huang, Yi Zhou, Li Liu +2
Batch normalization (BN) is an important technique commonly incorporated into deep learning models to perform standardization within mini-batches. The merits of BN in improving a m…
Invertible Zero-Shot Recognition Flows
Yuming Shen, Jie Qin, Lei Huang
Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the hardness of training with…
On the Number of Linear Regions of Convolutional Neural Networks
H. Xiong, L. Huang, M. Yu +3
One fundamental problem in deep learning is understanding the outstanding performance of deep Neural Networks (NNs) in practice. One explanation for the superiority of NNs is that…