activity
20202024
most citedMEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

41 citations · 104 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2024

EGAN: Efficient Training of Efficient GANs for Image-to-Image Translation

Yifan Gong, Zheng Zhan, Qing Jin +8

One highly promising direction for enabling flexible real-time on-device image editing is utilizing data distillation by leveraging large-scale text-to-image diffusion models to ge…

cs.LG202218 cited

SparCL: Sparse Continual Learning on the Edge

Zifeng Wang, Zheng Zhan, Yifan Gong +7

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the t…

cs.CV20228 cited

Reverse Engineering of Imperceptible Adversarial Image Perturbations

Yifan Gong, Yuguang Yao, Yize Li +4

It has been well recognized that neural network based image classifiers are easily fooled by images with tiny perturbations crafted by an adversary. There has been a vast volume of…

cs.LG202141 cited

MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge

Geng Yuan, Xiaolong Ma, Wei Niu +13

Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…

cs.LG202011 cited

BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method

Xiaolong Ma, Zhengang Li, Yifan Gong +8

Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem. Prior works utilize l1-based group lasso or dynamic regularization such…

cs.SD202012 cited

RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition

Peiyan Dong, Siyue Wang, Wei Niu +8

Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones. However, previous RNN compression techniqu…