56 citations · 164 across the 24 of their papers we have counts for
6 papers · 1 filter
Multi-Dimensional Model Compression of Vision Transformer
Zejiang Hou, Sun-Yuan Kung
Vision transformers (ViT) have recently attracted considerable attentions, but the huge computational cost remains an issue for practical deployment. Previous ViT pruning methods t…
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…
A compressive multi-kernel method for privacy-preserving machine learning
Thee Chanyaswad, J. Morris Chang, S. Y. Kung
As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserv…
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…
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network
Tanvir Mahmud, A. Q. M. Sazzad Sayyed, Shaikh Anowarul Fattah +1
Deep neural network is an effective choice to automatically recognize human actions utilizing data from various wearable sensors. These networks automate the process of feature ext…