Ji Lin

I am currently a third-year Ph.D. student at MIT EECS, advised by Prof. Song Han. My research interests lie in efficient deep learning algorithms and systems.

I received my B.Eng. in Electronic Engineering from Tsinghua University, and M.Sc. in EECS from MIT. I interned at Adobe Research with Jun-Yan Zhu and Richard Zhang during Summer 2020, and worked at OmniML during summer 2021.

News

• [11/2021] Contributed a chapter to book Advanced Methods and Deep Learning in Computer Vision.
• [09/2021] MCUNetV2 accepted to NeurIPS.
• [11/2020] Got into NVIDIA Graduate Fellowship Finalist.
• [09/2020] MCUNet and DiffAugment accepted to NeurIPS.
• [08/2020] Check out MCUNet! >70% ImageNet accuracy on off-the-shelf commercial microcontrollers.
• [08/2020] Our TSM repo receives 1k stars!

Publications [Full List]

* indicates equal contribution

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning Ji Lin, Wei-Ming Chen, Han Cai, Chuang Gan, Song Han NeurIPS 2021 / arXiv / Project Page Press: MIT News / TechTalks / TheNextWeb / AIM

Anycost GANs for Interactive Image Synthesis and Editing Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu CVPR 2021 / arXiv / Project Page / Code / Video / Demo Video / Colab Tutorial

MCUNet: Tiny Deep Learning on IoT Devices Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han NeurIPS 2020 / arXiv / Project Page / Code / Demo Video Press: MIT News (homepage spotlight) / WIRED / MIT TR-China / IBM / Morning Brew / Stacey on IoT / Analytics Insight / Techable / Tendencias Spotlight Presentation

Differentiable Augmentation for Data-Efficient GAN Training Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han NeurIPS 2020 / arXiv / Project Page / Code / Slides / Colab Tutorial Press: VentureBeat

GAN Compression: Efficient Architectures for Interactive Conditional GANs Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, Song Han CVPR 2020 / arXiv / Project Page / Code / Video / Demo Video / Slides

APQ: Joint Search for Network Architecture, Pruning and Quantization Policy Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Hanrui Wang, Yujun Lin, Song Han CVPR 2020 / arXiv

AutoML for Architecting Efficient and Specialized Neural Networks Han Cai*, Ji Lin*, Yujun Lin*, Zhijian Liu*, Kuan Wang*, Tianzhe Wang*, Ligeng Zhu*, Song Han IEEE Micro

TSM: Temporal Shift Module for Efficient Video Understanding Ji Lin, Chuang Gan, Song Han ICCV 2019 / arXiv Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos Ji Lin, Chuang Gan, Song Han NeurIPS 2019 Workshop / arXiv Project Page / Demo / Code Press: MIT News / MIT Technology Review / WIRED / Engadget/ NVIDIA News / Industry Integration@NVIDIA

HAQ: Hardware-Aware Automated Quantization Kuan Wang*, Zhijian Liu*, Yujun Lin*, Ji Lin, Song Han CVPR 2019 / arXiv Oral Presentation Hardware-Centric AutoML for Mixed-Precision Quantization Kuan Wang*, Zhijian Liu*, Yujun Lin*, Ji Lin, Song Han IJCV Project Page / Code

Defensive Quantization: When Efficiency Meets Robustness Ji Lin, Chuang Gan, Song Han ICLR 2019 / arXiv / MIT News

AMC: AutoML for Model Compression and Acceleration on Mobile Devices Yihui He*, Ji Lin*, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han ECCV 2018 / arXiv / Project Page / Poster / Code

Runtime Neural Pruning Ji Lin*, Yongming Rao*, Jiwen Lu, Jie Zhou NIPS 2017 Runtime Network Routing for Efficient Image Classification Yongming Rao, Jiwen Lu, Ji Lin, Jie Zhou T-PAMI