56 citations · 99 across the 17 of their papers we have counts for
10 papers · 1 filter
CHEX: CHannel EXploration for CNN Model Compression
Zejiang Hou, Minghai Qin, Fei Sun +7
Channel pruning has been broadly recognized as an effective technique to reduce the computation and memory cost of deep convolutional neural networks. However, conventional pruning…
Class-Discriminative CNN Compression
Yuchen Liu, David Wentzlaff, S. Y. Kung
Compressing convolutional neural networks (CNNs) by pruning and distillation has received ever-increasing focus in the community. In particular, designing a class-discrimination ba…
Few-shot Learning via Dependency Maximization and Instance Discriminant Analysis
Zejiang Hou, Sun-Yuan Kung
We study the few-shot learning (FSL) problem, where a model learns to recognize new objects with extremely few labeled training data per category. Most of previous FSL approaches r…
Content-Aware GAN Compression
Yuchen Liu, Zhixin Shu, Yijun Li +3
Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders th…
Rethinking Class-Discrimination Based CNN Channel Pruning
Yuchen Liu, David Wentzlaff, S. Y. Kung
Channel pruning has received ever-increasing focus on network compression. In particular, class-discrimination based channel pruning has made major headway, as it fits seamlessly w…
Soft-Root-Sign Activation Function
Yuan Zhou, Dandan Li, Shuwei Huo +1
The choice of activation function in deep networks has a significant effect on the training dynamics and task performance. At present, the most effective and widely-used activation…