Yujun Shen is currently a third-year Ph.D. student in Multi-Media Lab (MMLab), The Chinese University of Hong Kong (CUHK), supervised by Prof. Xiaoou Tang and Prof. Bolei Zhou. He received his bachelor’s degrees of Electronic Engineering and Management in Tsinghua University in 2016. His research interests lie in Computer Vision, Deep Learning, Generative Model, Model Compression and 3D Vision.
- [2020/03] Our research group GenForce is online!
- [2020/02] Two papers got accepted by CVPR 2020!
- [2019/05] Join Facebook Reality Lab (FRL) as a research intern!
- [2018/03] One paper got accepted by CVPR 2018!
Ph.D. Student in Department of Information Engineering
This project aims at applying deep learning model on Fisheye images.
This work aims at applying pre-trained GAN models to real image processing. Multiple latent codes are employed to invert a given image, whose feature maps are then composed at some intermediate layer of the generator. Such over-parameterization significantly advances GAN inversion task and enables a variety of real-world applications by reusing the rich knowledge GAN models have learned.
This work conducts detailed analysis on the layer-wise generative representation of GANs for scene synthesis. It turns out that GANs spontaneously learn hierarchical semantics, including layout (bottom layers), categorical objects (middle layers), and scene attributes (top layers). A re-scoring technique is further proposed to identify the semantics most relevant to the pre-trained model from a broad candidate set.
[Paper] [Project Page] [Demo]
This work proposes Residual Knowledge Distillation (RKD) by distilling the knowledge from teacher model twice. Besides the student model, an assistant model is introduced to learn the residual error between the student and the teacher for better performance.
This work improves KD method by proposing Stage-by-Stage Knowledge Distillation (SSKD). In particular, SSKD introduces progressive training manner and does not rely on the loss weight for training. It shows strong superiority and robustness on various tasks, including image classification, image detection, and face recognition.
This work proposes FaceFeat-GAN to balance the trade-off between image quality and diversity in conditional GAN. Specially, it first generates semantic facial features as intermediate outputs with a collection of feature generators, and then maps these features to photo-realistic image. A novel hierarchical adversarial competition is proposed to help all components collaborate together.
This work proposes FaceID-GAN that achieves high-quality identity-preserving face synthesis by introducing an additional player to conventional GAN model. Specially, the generator competes with not only the discriminator but also the classifier. FaceID-GAN also involves information symmetry in the training process to make sure the knowledge fed into the generator is consistent with the supervision of generator.
- Hong Kong Ph.D. Fellowship, Hong Kong Research Grants Council, 2016
- Excellent Graduate, Tsinghua University, 2016
- Excellent Graduation Project, Tsinghua University, 2016
- Academic Excellence Scholarship, Tsinghua University, 2014, 2015
- Honorable Mention, The Mathematical Contest in Modeling (MCM), 2014
- First prize, National Competition in Physics for College Students, 2013
- National Scholarship, Ministry of Education of China, 2013
- A member of "Project for Leadership", Tsinghua University, 2012
- Programming Language: Python, C/C++
- Deep Learning Framework: Tensorflow, PyTorch, Caffe
- Tools: Github, LaTex, Office
- Language: Mandarin, English
- Hobbies: Soccer, Badminton, Singing