Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Ji Lin

Ji Lin

Anycost GANs for Interactive Image Synthesis and Editing


Mar 04, 2021
Ji Lin, Richard Zhang, Frieder Ganz, Song Han, Jun-Yan Zhu

Figure 1 for Anycost GANs for Interactive Image Synthesis and Editing
Figure 2 for Anycost GANs for Interactive Image Synthesis and Editing
Figure 3 for Anycost GANs for Interactive Image Synthesis and Editing
Figure 4 for Anycost GANs for Interactive Image Synthesis and Editing
* Accepted to CVPR 2021. The code and demo are available: https://github.com/mit-han-lab/anycost-gan 
  Access Paper or Ask Questions

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution


Aug 13, 2020
Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, Song Han

Figure 1 for Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Figure 2 for Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Figure 3 for Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Figure 4 for Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
* ECCV 2020. The first two authors contributed equally to this work. Project page: http://spvnas.mit.edu/ 
  Access Paper or Ask Questions

Hardware-Centric AutoML for Mixed-Precision Quantization


Aug 11, 2020
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

Figure 1 for Hardware-Centric AutoML for Mixed-Precision Quantization
Figure 2 for Hardware-Centric AutoML for Mixed-Precision Quantization
Figure 3 for Hardware-Centric AutoML for Mixed-Precision Quantization
Figure 4 for Hardware-Centric AutoML for Mixed-Precision Quantization
* International Journal of Computer Vision (IJCV), 2020 
 * Journal preprint of arXiv:1811.08886 (IJCV, 2020). The first three authors contributed equally to this work. Project page: https://hanlab.mit.edu/projects/haq/ 
  Access Paper or Ask Questions

MCUNet: Tiny Deep Learning on IoT Devices


Jul 20, 2020
Ji Lin, Wei-Ming Chen, Yujun Lin, John Cohn, Chuang Gan, Song Han

Figure 1 for MCUNet: Tiny Deep Learning on IoT Devices
Figure 2 for MCUNet: Tiny Deep Learning on IoT Devices
Figure 3 for MCUNet: Tiny Deep Learning on IoT Devices
Figure 4 for MCUNet: Tiny Deep Learning on IoT Devices
* Demo video available here: https://youtu.be/YvioBgtec4U 
  Access Paper or Ask Questions

Differentiable Augmentation for Data-Efficient GAN Training


Jun 18, 2020
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han

Figure 1 for Differentiable Augmentation for Data-Efficient GAN Training
Figure 2 for Differentiable Augmentation for Data-Efficient GAN Training
Figure 3 for Differentiable Augmentation for Data-Efficient GAN Training
Figure 4 for Differentiable Augmentation for Data-Efficient GAN Training

  Access Paper or Ask Questions

APQ: Joint Search for Network Architecture, Pruning and Quantization Policy


Jun 15, 2020
Tianzhe Wang, Kuan Wang, Han Cai, Ji Lin, Zhijian Liu, Song Han

Figure 1 for APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Figure 2 for APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Figure 3 for APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
Figure 4 for APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
* Accepted by CVPR 2020 
  Access Paper or Ask Questions

Lite Transformer with Long-Short Range Attention


Apr 24, 2020
Zhanghao Wu, Zhijian Liu, Ji Lin, Yujun Lin, Song Han

Figure 1 for Lite Transformer with Long-Short Range Attention
Figure 2 for Lite Transformer with Long-Short Range Attention
Figure 3 for Lite Transformer with Long-Short Range Attention
Figure 4 for Lite Transformer with Long-Short Range Attention
* ICLR 2020. The first two authors contributed equally to this work 
  Access Paper or Ask Questions

GAN Compression: Efficient Architectures for Interactive Conditional GANs


Mar 19, 2020
Muyang Li, Ji Lin, Yaoyao Ding, Zhijian Liu, Jun-Yan Zhu, Song Han

Figure 1 for GAN Compression: Efficient Architectures for Interactive Conditional GANs
Figure 2 for GAN Compression: Efficient Architectures for Interactive Conditional GANs
Figure 3 for GAN Compression: Efficient Architectures for Interactive Conditional GANs
Figure 4 for GAN Compression: Efficient Architectures for Interactive Conditional GANs

  Access Paper or Ask Questions

Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos


Oct 01, 2019
Ji Lin, Chuang Gan, Song Han

Figure 1 for Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
Figure 2 for Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
Figure 3 for Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
Figure 4 for Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos

  Access Paper or Ask Questions

Design Automation for Efficient Deep Learning Computing


Apr 24, 2019
Song Han, Han Cai, Ligeng Zhu, Ji Lin, Kuan Wang, Zhijian Liu, Yujun Lin

Figure 1 for Design Automation for Efficient Deep Learning Computing
Figure 2 for Design Automation for Efficient Deep Learning Computing
Figure 3 for Design Automation for Efficient Deep Learning Computing
Figure 4 for Design Automation for Efficient Deep Learning Computing

  Access Paper or Ask Questions

Defensive Quantization: When Efficiency Meets Robustness


Apr 17, 2019
Ji Lin, Chuang Gan, Song Han

Figure 1 for Defensive Quantization: When Efficiency Meets Robustness
Figure 2 for Defensive Quantization: When Efficiency Meets Robustness
Figure 3 for Defensive Quantization: When Efficiency Meets Robustness
Figure 4 for Defensive Quantization: When Efficiency Meets Robustness

  Access Paper or Ask Questions

HAQ: Hardware-Aware Automated Quantization


Dec 06, 2018
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, Song Han

Figure 1 for HAQ: Hardware-Aware Automated Quantization
Figure 2 for HAQ: Hardware-Aware Automated Quantization
Figure 3 for HAQ: Hardware-Aware Automated Quantization
Figure 4 for HAQ: Hardware-Aware Automated Quantization
* The first three authors contributed equally to this work. Project page: https://hanlab.mit.edu/projects/haq/ 
  Access Paper or Ask Questions

Joint Monocular 3D Vehicle Detection and Tracking


Dec 02, 2018
Hou-Ning Hu, Qi-Zhi Cai, Dequan Wang, Ji Lin, Min Sun, Philipp Krähenbühl, Trevor Darrell, Fisher Yu

Figure 1 for Joint Monocular 3D Vehicle Detection and Tracking
Figure 2 for Joint Monocular 3D Vehicle Detection and Tracking
Figure 3 for Joint Monocular 3D Vehicle Detection and Tracking
Figure 4 for Joint Monocular 3D Vehicle Detection and Tracking
* 14 pages, 11 figures. Fix table values misplacement and typos. All the results unchanged 
  Access Paper or Ask Questions

Temporal Shift Module for Efficient Video Understanding


Nov 20, 2018
Ji Lin, Chuang Gan, Song Han

Figure 1 for Temporal Shift Module for Efficient Video Understanding
Figure 2 for Temporal Shift Module for Efficient Video Understanding
Figure 3 for Temporal Shift Module for Efficient Video Understanding
Figure 4 for Temporal Shift Module for Efficient Video Understanding
* Our framework ranks the first on both Something-Something V1 and V2 leaderboards upon this paper's submission. Measured on P100 GPU, our single model achieved 1.8% higher accuracy at 8x lower latency and 12x higher throughput compared to I3D 
  Access Paper or Ask Questions

AMC: AutoML for Model Compression and Acceleration on Mobile Devices


Aug 26, 2018
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, Song Han

Figure 1 for AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Figure 2 for AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Figure 3 for AMC: AutoML for Model Compression and Acceleration on Mobile Devices
Figure 4 for AMC: AutoML for Model Compression and Acceleration on Mobile Devices

  Access Paper or Ask Questions

Reinforcement Learning from Imperfect Demonstrations


Feb 14, 2018
Yang Gao, Huazhe, Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell

Figure 1 for Reinforcement Learning from Imperfect Demonstrations
Figure 2 for Reinforcement Learning from Imperfect Demonstrations
Figure 3 for Reinforcement Learning from Imperfect Demonstrations
Figure 4 for Reinforcement Learning from Imperfect Demonstrations

  Access Paper or Ask Questions