activity
20182026
most citedDrawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks

48 citations · 208 across the 32 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV2026

HiLo-Token: Input-Adaptive High-Low Frequency Token Compression for Efficient Image Editing

Haoran You, Yotam Nitzan, Lingzhi Zhang +8

Creative image editing tools, such as Adobe Photoshop's Remove and Generative Fill buttons, are central to everyday customer use and account for a major share of traffic. However,…

cs.CV2025

Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training

Lexington Whalen, Zhenbang Du, Haoran You +4

Training diffusion models (DMs) requires substantial computational resources due to multiple forward and backward passes across numerous timesteps, motivating research into efficie…

cs.CV2023

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…

cs.CV2022★ 3 cited

Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference

Haoran You, Yunyang Xiong, Xiaoliang Dai +5

Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs'…

cs.CV2022

SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning

Haoran You, Baopu Li, Zhanyi Sun +2

Neural architecture search (NAS) has demonstrated amazing success in searching for efficient deep neural networks (DNNs) from a given supernet. In parallel, the lottery ticket hypo…

cs.CV2020★ 30 cited

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training

Yonggan Fu, Haoran You, Yang Zhao +5

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendous demand for intelligent edge devices featuring on-site learning, while the practical realization of such…