10 citations · 13 across the 7 of their papers we have counts for
7 papers
NetDistiller: Empowering Tiny Deep Learning via In-Situ Distillation
Shunyao Zhang, Yonggan Fu, Shang Wu +4
Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployments of TNNs on edge devices which are constrained by strict li…
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Yonggan Fu, Ye Yuan, Souvik Kundu +3
Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and…
Hint-Aug: Drawing Hints from Foundation Vision Transformers Towards Boosted Few-Shot Parameter-Efficient Tuning
Zhongzhi Yu, Shang Wu, Yonggan Fu +2
Despite the growing demand for tuning foundation vision transformers (FViTs) on downstream tasks, fully unleashing FViTs' potential under data-limited scenarios (e.g., few-shot tun…
Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design
Yonggan Fu, Zhifan Ye, Jiayi Yuan +4
Novel view synthesis is an essential functionality for enabling immersive experiences in various Augmented- and Virtual-Reality (AR/VR) applications, for which generalizable Neural…
EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm & Accelerator Co-Design
Haoran You, Cheng Wan, Yang Zhao +11
Eye tracking has become an essential human-machine interaction modality for providing immersive experience in numerous virtual and augmented reality (VR/AR) applications desiring h…
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Yonggan Fu, Shunyao Zhang, Shang Wu +2
Vision transformers (ViTs) have recently set off a new wave in neural architecture design thanks to their record-breaking performance in various vision tasks. In parallel, to fulfi…
Max-Affine Spline Insights Into Deep Network Pruning
Haoran You, Randall Balestriero, Zhihan Lu +6
In this paper, we study the importance of pruning in Deep Networks (DNs) and the yin & yang relationship between (1) pruning highly overparametrized DNs that have been trained from…