About Me

(Nov. 2022 Update: It seems the https service has expried recently for my website. If you see a REALLY BIG selfie of me (sorry about this, I’m not a narcissist..), that means you are probably using the https instead of http to access my website (will fix this soon!) Click here to switch to the normal view.)

My name is Huan Wang (Chinese: 王欢. My first name means “happiness/joy” in Chinese, a simple and ultimate wish from my parents). I am now a 4th-year Ph.D. candidate at SMILE Lab of Northeastern University (Boston, USA), advised by Prof. Yun (Raymond) Fu. Before that, I received my M.S. and B.E. degrees from Zhejiang University (Hangzhou, China) in 2019 and 2016, respectively, 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 been very lucky to work with fatanstic industrial guys from Alibaba Group / MERL / Snap during my summer internships.

I am interested in a variety of topics in computer vision and machine learning. My research works orbit efficient deep learning (a.k.a. model compression), spanning from the most common image classifcation task (GReg, Awesome-PaI, TPP) to neural style transfer (Collaborative-Distillation), single image super-resolution (ASSL, SRP), and 3D novel view synthesis / NeRF (R2L, MobileR2L).

I do my best towards easily reproducible research.


  • 2023.03: [Award] Received ICLR’23 travel award. Thanks to ICLR! I am going to attend ICLR’23 in Kigali Rwanda to present our papers (Trainability Preserving Neural Pruning, Image as Set of Points). Look forward to seeing you there if you also attend!
  • 2023.02: [CVPR’23] Two papers accepted by CVPR’23: 1. MobileR2L: [Arxiv] [Code] (🎉congrats to Junli) 2. Frame Flexible Network (🎉congrats to Yitian)
  • 2023.01: [ICLR’23] Two papers accpeted by ICLR’23: Trainabaility Preserveing Neural Pruning and Image as Set of Points (Oral, top-5%).
  • 2023.01: [Internship] Start part-time research internship at Snap, luckily working with the Creative Vision team again.
  • 2023.01: [Preprint] 🔥Check out our preprint work that deciphers the so confusing benchmark situation in neural network (filter) pruning: [Arxiv] [Code] (to release)
  • 2022.12: [Preprint] 🔥Check out our new blazing fast🚀 neural rendering model on mobile devices: MobileR2L can render 1008x756 images at 56fps on iPhone13 [Arxiv] [Code] – This paper is accpeted by CVPR’23!
  • 2022.12: Recognized among 2022 Snap Fellowship Honorable Mentions. Thanks to Snap!
  • 2022.10: [Award] Received the NeurIPS’22 Scholar Award. Thanks to NeurIPS!
  • 2022.09: [NeurIPS’22] 3 papers accepted by NeurIPS’22: One on my lead (it 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!
  • 2022.04: [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 paper (<3%)! [PyTorch Code]
  • 2021.06: [Internship] 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] [PyTorch Code]
  • 2020.06: [Internship] Start summer internship at MERL, working with Dr. Mike Jones and Dr. Suhas Lohit. (09/2022 Update: Finally, paper of this project accpeted by NeurIPS’22. Thanks to my co-authors and the reviewers!)
  • 2020.02: [CVPR’20] One paper about model compression for ultra-resolution neural style transfer is accepted by CVPR 2020. Code released here.
  • 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 accepted by IEEE JSTSP.
  • 2019.09: Join SMILE Lab at NEU (Boston, USA) to pursue my Ph.D. degree.
  • 2019.07: [Internship’19] Start summer internship at Taobao of Alibaba Group at Hangzhou, China.
  • 2019.06: Graduate with M.Sc. degree from Zhejiang University, Hangzhou, China.


Huan Wang, Can Qin, Yue Bai, Yun Fu. "Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning". Preprint, 2023.

Selected Publications

Junli Cao, Huan Wang, Pavlo Chemerys, Vladislav Shakhrai, Ju Hu, Yun Fu, Denys Makoviichuk, Sergey Tulyakov, Jian Ren.
"Real-Time Neural Light Field on Mobile Devices".
CVPR, 2023.
Project Webpage
Huan Wang, Yun Fu.
"Trainability Preserving Neural Pruning".
In ICLR, 2023.
PyTorch Code (to be updated)
Xu Ma, Yuqian Zhou, Huan Wang, Can Qin, Bin Sun, Chang Liu, Yun Fu.
"Image as Set of Points".
In ICLR (Oral, 5%), 2023.
PyTorch Code
Huan Wang, Suhas Lohit, Mike Jones, Yun Fu.
"What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective".
In NeurIPS, 2022.
Project Webpage
PyTorch Code
Yue Bai, Huan Wang, Xu Ma, Yitian Zhang, Zhiqiang Tao, Yun Fu.
"Parameter-Efficient Masking Networks".
In NeurIPS, 2022.
PyTorch Code
Yitian Zhang, Yue Bai, Huan Wang, Yi Xu, Yun Fu.
"Look More but Care Less in Video Recognition".
In NeurIPS, 2022.
Arxiv (Coming soon)
PyTorch Code
Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Menglei Chai, Yun Fu, Sergey Tulyakov.
"R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis".
In ECCV, 2022.
Project Webpage
PyTorch Code
Huan Wang, Can Qin, Yue Bai, Yulun Zhang, Yun Fu.
"Recent Advances on Neural Network Pruning at Initialization".
In IJCAI, 2022.
PyTorch Code
Huan Wang*, Yulun Zhang*, Can Qin, Yun Fu.
"Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning".
In ICLR, 2022. (*Equal Contribution)
Open Review
PyTorch Code
Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, Yun Fu.
"Dual Lottery Ticket Hypothesis".
In ICLR, 2022.
Open Review, Arxiv
PyTorch Code
Huan Wang*, Yulun Zhang*, Can Qin, Yun Fu.
"Aligned Structured Sparsity Learning for Efficient Image Super-Resolution".
In NeurIPS (Spotlight), 2021. (*Equal Contribution)
Camera Ready
PyTorch Code
Huan Wang, Can Qin, Yulun Zhang, Yun Fu.
"Neural Pruning via Growing Regularization".
In ICLR, 2021.
Arxiv, OpenReview
PyTorch Code
Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan Yang.
"Collabrotive Distillation for Ultra-Resolution Universal Style Transfer".
In CVPR, 2020.
Arxiv, Camera Ready
PyTorch Code
Xiaotang Jiang, Huan Wang, Yiliu Chen, Ziqi Wu, et al.
"MNN: A Universal and Efficient Inference Engine".
In MLSys (Oral), 2020.
Huan Wang, Xinyi Hu, Qiming Zhang, Yuehai Wang, Lu Yu, Haoji Hu.
"Structured Pruning for Efficient ConvNets via Incremental Regularization".
In NeurIPS Workshop, 2018; IJCNN, 2019 (Oral); Journal extension to IEEE JSTSP, 2019.
NeurIPS Workshop, IJCNN, JSTSP
Caffe Code
Huan Wang*, Qiming Zhang*, Yuehai Wang, Haoji Hu.
"Structured Probabilistic Pruning for Convolutional Neural Network Acceleration".
In BMVC, 2018 (Oral).
Arxiv, Camera Ready
Caffe Code


Academic Services

  • Journal Reviewer: IJCV, TIP, TNNLS, JSTSP, Neurocomputing, etc.
  • Conference Reviewer: CVPR’22, ECCV’22, ICML’22, NeurIPS’22, ICLR’22, AAAI’23, IJCAI’23, CVPR’23, MLSys’23, ICCV’23, etc.