Huan Wang
Welcome to my webpage!👋🏻 My name is Huan Wang (Chinese: 王欢. My first name means "happiness or
joy" in Chinese). I am a Tenure-Track Assistant Professor at Westlake University (Hangzhou,
China), School of Engineering, AI Program. I did my Ph.D. (2024) at Northeastern University (Boston,
USA), advised by Prof. Yun (Raymond) Fu at SMILE Lab. Before that,
I did my M.S. (2019) and B.E. (2016) at Zhejiang University (Hangzhou, China), advised
by Prof. Haoji
Hu. I also visited VLLab at University of
California, Merced, luckily working with Prof. Ming-Hsuan Yang. I interned at Google /
Snap / MERL / Alibaba. I also actively collaborate with other industrial researchers from
Salesforce, Meta, etc.
My research orbits Efficient AI,
spanning image classifcation / detection / segmentation tasks [GReg, Pruning-at-Initialization
Survey, TPP] to neural style transfer [Collaborative-Distillation],
single image super-resolution [ASSL/GASSL, SRP, ISS-P], 3D novel view synthesis / neural rendering /
NeRF / NeLF [R2L, MobileR2L], text-to-image generation /
diffusion models / Stable Diffusion [SnapFusion], and snapshot compressive imaging
(SCI) [QuantizedSCI].
I also spent some time on easily
reproducible research.
My email: wanghuan [at] westlake [dot] edu [dot] cn
[ Google Scholar ]
[ GitHub ]
[ LinkedIn ]
[ Twitter ]
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Openings / Collaborations
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Hiring Ph.D. Students / Research Assistants / Visiting Students
: I am recruinting new Ph.D. students (for 2025 Fall), research assistants, and
visiting students for research-oritented projects on efficient deep learning (e.g., network
pruning, distillation, quantization), GenAI (e.g., T2I generation, diffusion models, LLM/MLLM),
digital human / neural rendering (e.g., avatars), etc. Welcome to check here (and 知乎) for more information if you are
interested!
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Recent News
- 2024/09 [NeurIPS'24] We introduce a training framework
Scala to learn slimmable ViTs. Using Scala, a ViT model is trained once but can inference
at different widths, up to the need of devices with different resources. The project is led by
Yitian. Congrats!
- 2024/07 [MM'24] We present the first real-time
on-device video SCI (Snapshot Compressive Imaging) framework via dedicated network design
and a distillation-based training strategy. Congrats to Miao! (Code will
be released soon.)
- 2024/07 [ECCV'24] One paper about efficient video SCI
(Snapshot Compressive Imaging) via network quantization is accepted by ECCV'24 as an
oral. Congrats to Miao! [Code]
- 2024/06 [New Start] Joined Westlake University as a
Tenure-Track Assistant Professor.
- 2024/03 [Graduation] 🎊🎈Successfully defended my
dissertation. So many thanks to my Ph.D. committee (Prof. Yun Raymond Fu, Prof. Octavia Camps, Prof.
Zhiqiang Tao) and co-authors!
- 2024/01 [ICLR'24] One paper proposing a simple and
effective data augmentation method for improved action recognition is accepted by ICLR'24.
Congrats to Yitian!
- 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. 2024/02
Gave a talk at ASU about SnapFusion
[Slides]. Thanks for the warm invitation from Maitreya Jitendra Patel, Sheng Cheng, and Prof. Yezhou Yang!
- 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]
[Slides].
- 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).
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Towards Real-time Video Compressive Sensing on Mobile Devices
Miao Cao, Lishun
Wang, Huan Wang, Guoqing Wang, Xin
Yuan
In ACM MM, 2024
ArXiv
Code
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A Simple Low-bit Quantization Framework for Video Snapshot
Compressive Imaging
Miao Cao,
Lishun Wang, Huan Wang, Xin
Yuan
In ECCV (Oral), 2024
Arxiv
Code
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Don't Judge by the Look: A Motion Coherent Augmentation
for Video Recognition
Yitian Zhang, Yue Bai, Huan
Wang, Yizhou Wang, Yun Fu
In ICLR, 2024
ArXiv
Code
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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 (may not release in a short period due to IP issues)
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Iterative Soft Shrinkage Learning for Efficient Image
Super-Resolution
Jiamian
Wang, Huan Wang, Yulun Zhang, Yun Fu, Zhiqiang Tao
In ICCV, 2023
Arxiv
Code
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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
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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
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Real-Time Neural Light Field on Mobile
Devices
Junli Cao, Huan Wang, Pavlo Chemerys, Vladislav Shakhray, Ju
Hu, Yun Fu, Denys Makoviichuk,
Sergey Tulyakov, Jian Ren
In CVPR, 2023
Project Webpage
ArXiv
Code
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Trainability Preserving Neural Pruning
Huan Wang, Yun Fu
In ICLR, 2023
OpenReivew
ArXiv
PyTorch Code
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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
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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
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Parameter-Efficient Masking Networks
Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, Yun
Fu
In NeurIPS, 2022
ArXiv
PyTorch Code
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Look More but Care Less in Video Recognition
Yitian Zhang, Yue Bai, Huan
Wang, Yi Xu, Yun Fu
In NeurIPS, 2022
ArXiv
PyTorch Code
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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
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Recent Advances on Neural Network Pruning at
Initialization
Huan Wang, Can Qin, Yue Bai, Yulun Zhang, Yun Fu
In IJCAI, 2022
ArXiv / Slides
Paper
Collection
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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
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Dual Lottery Ticket Hypothesis
Yue Bai, Huan
Wang, Zhiqiang Tao, Kunpeng
Li, Yun Fu
In ICLR, 2022
Open Review, ArXiv
PyTorch Code
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Aligned Structured Sparsity Learning for
Efficient Image Super-Resolution
Huan Wang*, Yulun
Zhang*, Can Qin, Yun Fu
(*Equal Contribution)
In NeurIPS (Spotlight, <3%< /font>), 2021
Camera Ready
PyTorch Code
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Neural Pruning via Growing Regularization
Huan Wang, Can Qin, Yulun Zhang, Yun Fu
In ICLR, 2021
ArXiv, OpenReview
PyTorch Code
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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
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MNN: A Universal and Efficient Inference
Engine
Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, et al.
In MLSys (Oral), 2020
ArXiv
Code
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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
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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
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Professional Services
- Journal Reviewer - TPAMI, IJCV, TIP, TNNLS, PR, IoT, JSTSP, TCSVT,
etc.
- Conference Reviewer - CVPR'22-24, ECCV/ICCV'22-24, ICML'22/24, ICLR'22/24, NeurIPS'22-23,
AAAI'22-24, IJCAI'23-24, MLSys'23, SIGGRAPH Asia'23, WACV'24, FG'24, AISTATS'24, CPAL'24, ACCV'24,
etc.
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(This webpage template is stolen from Jon
Barron.)
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