Huan Wang's Website

Huan Wang

Welcome to my webpage! My name is Huan Wang (Chinese: 王欢. My first name means "happiness or joy" in Chinese, a simple and ultimate wish from my parents). I am now a final-year Ph.D. candidate at SMILE Lab, Northeastern University (Boston, USA), advised by Prof. Yun (Raymond) Fu. Before that, I received my M.S. (2019) and B.E. (2016) degrees from Zhejiang University (Hangzhou, China), advised by Prof. Haoji Hu. During 2018 summer, I visited VLLab at University of California, Merced, luckily working with Prof. Ming-Hsuan Yang. I have also actively collaborated with fatanstic industrial researchers from Google / Snap / MERL / Meta / Alibaba, etc.

My research works orbit Efficient Deep Learning (a.k.a. Model Compression), spanning the common image classifcation / detection / segmentation tasks (GReg, Pruning-at-Initialization Survey, TPP) to neural style transfer (Collaborative-Distillation), single image super-resolution (ASSL/GASSL, SRP), 3D novel view synthesis / neural rendering / NeRF / NeLF (R2L, MobileR2L), and text-to-image generation / diffusion models / Stable Diffusion (SnapFusion).

I do my best towards easily reproducible research.

I am on the job market of 2024. Looking for an industrial Full-Time Research Scientist / Research Engineer position in (1) efficient 3D neural modeling / reconstruction / rendering for digital human (e.g., avatars) etc., (2) efficient GenAI (text-to-image, image-to-image, text-to-video, etc.).

Google Scholar / GitHub / LinkedIn / Twitter

Open Positions

Ph.D./M.S./Visiting Students: Prof. Yu Yin will join the Department of Computer and Data Science at Case Western Reserve University (CWRU) as a Tenure-Track AP in Fall 2023. She is recruiting new Ph.D./M.S./visiting students, working on generative AI problems. Welcome to contact her if you are interested!

News

2023/11: [Award] Received Northeastern PhD Network Travel Award. Thanks to Northeastern!
2023/10: [Award] Received NeurIPS'23 Scholar Award. Thanks to NeurIPS!
2023/09: [NeurIPS'23] Two papers accepted by NeurIPS 2023: SnapFusion and a work about weakly-supervised latent graph inference led by Jianglin.
2023/07: [ICCV'23] One paper about sparse network training for efficient image super-resolution is accepted by ICCV'23. Congrats to Jiamian!
2023/06: [Internship'23] Start full-time summer internship at Google AR.
2023/06: [Preprint] 🔥🔥We are excited to present SnapFusion, a brand-new efficient Stable Diffusion model on mobile devices. SnapFusion can generate a 512x512 image from text in only 1.84s on iPhone 14 Pro, which is the fastest🚀 on-device SD model as far as we know! Give a talk at JD Health about this work [Slides].
2023/05: [Talk] Give a talk at ZJUI (ZJU-UIUC Institute) on the recent advances in efficient neural light field (NeLF), featuring two of our recent NeLF papers (R2L and MobileR2L). [Slides]
2023/05: [Award] Recognized as CVPR'23 Outstanding Reviewer (3.3%). Thanks to CVPR and the ACs!
2023/04: [TPAMI'23] Extension of our NeurIPS'21 spotlight paper ASSL, "Global Aligned Structured Sparsity Learning for Efficient Image Super-Resolution", is accepted by TPAMI (IF=24.31). Code will be relased to the same repo.
2023/03: [Award] Received ICLR'23 Travel Award. Thanks to ICLR!
2023/02: [CVPR'23] 2 papers accepted by CVPR'23: (1) MobileR2L [Code] , congrats to Junli! MobileR2L is a blazing fast🚀 neural rendering model designed for mobile devices: It can render 1008x756 images at 56fps on iPhone13. (2) Frame Flexible Network [Code], congrats to Yitian!
2023/01: [ICLR'23] 2 papers accpeted by ICLR'23: Trainabaility Preserveing Neural Pruning (TPP) and Image as Set of Points (Oral, top-5%).
2023/01: [Internship'23] Start part-time internship at Snap, luckily working with the Creative Vision team again. 2023/09 Update: The paper of this internship, SnapFusion, is accepted by NeurIPS 2023! Thanks to my coauthors!🎉
2023/01: [Preprint] Check out our preprint work that deciphers the so confusing benchmark situation in neural network (filter) pruning: Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning [Code]. Also give a talk @UT Austin about this work. Thanks for the warm invitation from Dr. Shiwei Liu and Prof. Atlas Wang!
2022/10: [Award] Received NeurIPS'22 Scholar Award. Thanks to NeurIPS!
2022/09: [NeurIPS'22] 3 papers accepted by NeurIPS'22: One under my lead (which was my 1st internship work at MERL in 2020 summer. Rejected 4 times. Now finally I close the loop. Thanks to my co-authors and the reviewers!), two collaborations. Code: Good-DA-in-KD, PEMN, AFNet.
2022/09: [TIP'22] One journal paper "Semi-supervised Domain Adaptive Structure Learning" accepted by TIP. Congtrats to Can!
2022/07: [ECCV'22] We present the first residual MLP network to represent neural light field (NeLF) for efficient novel view synthesis. Check our webpage and ArXiv!
2022/05: [Award] Received Northeastern PhD Network Travel Award. Thanks to Northeastern!
2022/04: [IJCAI'22] We offer the very first survey paper on Pruning at Initialization, accepted by IJCAI'22 [ArXiv] [Paper Collection].
2022/01: [ICLR'22] Two papers on neural network sparsity accepted by ICLR'22. One is about efficient image super-resolution (SRP), the other about lottery ticket hypothsis (DLTH).
2021/09: [NeurIPS'21] One paper on efficient image super-resolution is accepted by NeurIPS'21 as a Spotlight (<3%) paper! [Code]
2021/06: [Internship'21] Start summer internship at Snap Inc., working with the fantastic Creative Vision team.
2021/01: [ICLR'21] One paper about neural network pruning accepted by ICLR'21 as poster. [ArXiv] [Code]
2020/06: [Internship'20] Start summer internship at MERL, working with Dr. Mike Jones and Dr. Suhas Lohit. (2022/09 Update: Finally, paper of this project accpeted by NeurIPS'22 -- two good years have passed, thank God..!)
2020/02: [CVPR'20] One paper about model compression for ultra-resolution neural style transfer "Collaborative Distillation for Ultra-Resolution Universal Style Transfer" is accepted by CVPR'20 [Code].
2020/01: [MLSys'20] 2019 summer intern paper accepted by MLSys 2020. (Project: MNN from Alibaba, one of the fastest mobile AI engines on this planet. Welcome trying!)
2019/12: [JSTSP'19] One journal paper "Structured Pruning for Efficient Convolutional Neural Networks via Incremental Regularization" accepted by IEEE JSTSP.
2019/09: Join SMILE Lab at Northeastern University (Boston, USA) to pursue my Ph.D. degree.
2019/07: [Internship'19] Start summer internship at Taobao of Alibaba Group ( Hangzhou, China).
2019/06: Graduate with M.Sc. degree from Zhejiang University (Hangzhou, China).

My Research [Google Scholar]

	SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds Yanyu Li, Huan Wang*, Qing Jin, Ju Hu, Pavlo Chemerys, Yun Fu, Yanzhi Wang, Sergey Tulyakov, Jian Ren* (Equal Contribution) In NeurIPS*, 2023 ArXiv Project Webpage Code (to be released)
	Latent Graph Inference with Limited Supervision Jianglin Lu, Yi Xu, Huan Wang, Yue Bai, Yun Fu In *NeurIPS*, 2023 Camera Ready (to be updated) Code (to be released)
	Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution Jiamian Wang, Huan Wang, Yulun Zhang, Yun Fu, Zhiqiang Tao In *ICCV*, 2023 Arxiv Code
	Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning Huan Wang, Can Qin, Yue Bai, Yun Fu *Preprint*, 2023 ArXiv Code
	Global Aligned Structured Sparsity Learning for Efficient Image Super-Resolution Huan Wang, Yulun Zhang, Can Qin, Luc Van Gool, Yun Fu (Equal Contribution) TPAMI*, 2023 PDF Code
	Real-Time Neural Light Field on Mobile Devices Junli Cao, Huan Wang, Pavlo Chemerys, Vladislav Shakhrai, Ju Hu, Yun Fu, Denys Makoviichuk, Sergey Tulyakov, Jian Ren In *CVPR*, 2023 Project Webpage ArXiv Code
	Trainability Preserving Neural Pruning Huan Wang, Yun Fu In *ICLR*, 2023 OpenReivew ArXiv PyTorch Code
	Image as Set of Points Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu In *ICLR* (Oral, 5%), 2023 Project Webpage OpenReview PyTorch Code
	What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective Huan Wang, Suhas Lohit, Mike Jones, Yun Fu In *NeurIPS*, 2022 Project Webpage ArXiv PyTorch Code
	Parameter-Efficient Masking Networks Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, Yun Fu In *NeurIPS*, 2022 ArXiv PyTorch Code
	Look More but Care Less in Video Recognition Yitian Zhang, Yue Bai, Huan Wang, Yi Xu, Yun Fu In *NeurIPS*, 2022 ArXiv PyTorch Code
	R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Menglei Chai, Yun Fu, Sergey Tulyakov In *ECCV*, 2022 Project Webpage ArXiv PyTorch Code
	Recent Advances on Neural Network Pruning at Initialization Huan Wang, Can Qin, Yue Bai, Yulun Zhang, Yun Fu In *IJCAI*, 2022 ArXiv Paper Collection
	Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning Huan Wang, Yulun Zhang, Can Qin, Yun Fu (Equal Contribution) In ICLR*, 2022 Open Review PyTorch Code
	Dual Lottery Ticket Hypothesis Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, Yun Fu In *ICLR*, 2022 Open Review, ArXiv PyTorch Code
	Aligned Structured Sparsity Learning for Efficient Image Super-Resolution Huan Wang, Yulun Zhang, Can Qin, Yun Fu (Equal Contribution) In NeurIPS* (Spotlight, <3%), 2021 Camera Ready PyTorch Code
	Neural Pruning via Growing Regularization Huan Wang, Can Qin, Yulun Zhang, Yun Fu In *ICLR*, 2021 ArXiv, OpenReview PyTorch Code
	Collabrotive Distillation for Ultra-Resolution Universal Style Transfer Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan Yang In *CVPR*, 2020 ArXiv, Camera Ready PyTorch Code
	MNN: A Universal and Efficient Inference Engine Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, et al In *MLSys* (Oral), 2020 ArXiv Code
	Structured Pruning for Efficient ConvNets via Incremental Regularization Huan Wang, Xinyi Hu, Qiming Zhang, Yuehai Wang, Lu Yu, Haoji Hu In *NeurIPS Workshop, 2018; IJCNN, 2019 (Oral); Journal extension to IEEE JSTSP*, 2019 NeurIPS Workshop, IJCNN, JSTSP Caffe Code
	Structured Probabilistic Pruning for Convolutional Neural Network Acceleration Huan Wang, Qiming Zhang, Yuehai Wang, Haoji Hu (Equal Contribution) In BMVC*, 2018 (Oral) ArXiv, Camera Ready Caffe Code

Invited Talks

[07/07/2023] 智东西: Recent Advances on Efficient Neural Light Field [Slides] [MobileR2L AR Demo1] [MobileR2L AR Demo2]
[06/21/2023] JD Health: SnapFusion: Text-to-Image Diffusion Model on Mobile Devices within Two Seconds [Slides]
[05/30/2023] Prof. Zuozhu Liu's Group @ZJUI: Recent Advances on Efficient Neural Light Field [Slides]
[01/06/2023] VITA Group @UT Austin: Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning [Slides]
[09/30/2021] Data Lab @NEU: Neural Pruning via Growing Regularization (ICLR'21) [Slides]

Professional Services

Journal Reviewer: TPAMI, IJCV, TIP, TNNLS, PR, IoT, JSTSP, TCSVT, Neurocomputing, NCAA, PLOS ONE, CIM, APIN, Advances in Statistical Analysis (AStA), Scientific Programming, The Computer Journal (COMPJ), MTAP, AI Communications (AIC), etc.
Conference Reviewer: CVPR'22-24, ECCV/ICCV'22-23, ICML'22, ICLR'22/24, NeurIPS'22-23, AAAI'22-24, IJCAI'23, MLSys'23, SIGGRAPH Asia'23, WACV'24, FG'24, AISTATS'24 etc.

(Stolen from Jon Barron)