Jiayun (Peter) Wang
PhD Candidate, | UC Berkeley |
Graduate Student Researcher, | BAIR and Vision Science |
Email: | |
CV | Google Scholar | GitHub | LinkedIn |
PhD Candidate, | UC Berkeley |
Graduate Student Researcher, | BAIR and Vision Science |
Email: | |
CV | Google Scholar | GitHub | LinkedIn |
I am a PhD candidate at UC Berkeley, advised by Prof. Stella Yu and Prof. Meng Lin. My research lies at the intersection of machine learning, computer vision and AI for healthcare.
I received my B.E in Electronic Engineering from Xi'an Jiaotong University in 2018, where I worked with Prof. Jinjun Wang at the Institute of Artificial Intelligence and Robotics.
News! I am on schedule to graduate in May 2023.
My research lies at the intersection of machine learning, computer vision and AI for healthcare.
Specifically, I study representation learning for real-world applications. Representation learning brings the data structures explicit and its advantages include robust model performance and high interpretability. Real-world data, compared to synthetic or curated data, usually comes with very few labels, and are noisy and imbalanced.
I study structure-aware representation learning to address these fundamental challenges in real world applications. First, it is aware of real-world data structures which are unlabeled and highly imbalanced and achieves robust performance compared to existing representation learning methods.
Second, it learns hierarchical structures of multi-level representations, such as high-level semantics, mid-level geometry and low-level materials, which leads to better model interpretation. Whereas existing methods are coarse and incomplete as they only learn high-level representations.
We demonstrate superior performance in real-world medical and ecological data.
See more details here.
Predicting demographics from meibography using deep learning
Jiayun Wang, Andrew D. Graham, Stella X. Yu, Meng C. Lin.
Nature - Scientific Reports 2022. [paper] [slides] [media coverage]
Open Long-Tailed Recognition in A Dynamic World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu.
IEEE TPAMI 2022. [paper][slides] [code]
Unsupervised Scene Sketch to Photo Synthesis
Jiayun Wang, Sangryul Jeon, Stella X. Yu, Xi Zhang, Himanshu Arora, Yu Lou.
ECCVW 2022. [project] [slides] [paper] [code] [media coverage]
3D Shape Reconstruction from Free-Hand Sketches
Jiayun Wang, Jierui Lin, Qian Yu, Runtao Liu, Yubei Chen, Stella X. Yu.
ECCVW 2022. [paper] [slides] [code]
Quantifying Meibomian Gland Morphology Using Artificial Intelligence
Jiayun Wang, Shixuan Li, Thao N. Yeh, Rudrasis Chakraborty, Andrew D Graham, Stella X. Yu, Meng C. Lin.
Optometry and Vision Science 2021. [paper]
Spatial Transformer for 3D Point Clouds
Jiayun Wang, Rudrasis Chakraborty, Stella X. Yu.
IEEE TPAMI 2021 [project] [video] [poster] [paper] [code]
We propose to learn different non-rigid transformations of the input point cloud for different local neighborhoods at each layer.
Orthogonal Convolutional Neural Networks
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, Stella X. Yu.
CVPR 2020. [project] [video] [slides] [paper] [code] [blog (Chinese)]
Orthogonal convolutional neural networks is a light-cost regularizer which reduces the feature redundancy and improves network performance and robustness under attack.
Sur-Real: Frechet Mean and Distance Transform for Complex-Valued Deep Learning
Rudrasis Chakraborty, Jiayun Wang, Stella X. Yu.
CVPRW 2019. Best Paper [paper] [poster] [code]
This work develops a novel deep learning architecture for naturally complex-valued data, with improved results and only 10% of the parameters as the baseline model.
A Deep Learning Approach for Meibomian Gland Atrophy Evaluation in Meibography Images
Jiayun Wang, Thao N. Yeh, Rudrasis Chakraborty, Stella X. Yu, Meng C. Lin.
Translational Vision Science and Technology 2019. [paper] [code]
This work develops a deep learning approach to digitally segmenting meibomian gland atrophy area and computing percent atrophy in meibography images.
Large-scale Long-Tailed Recognition in an Open World
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, Stella X. Yu.
CVPR 2019 Oral. [project] [paper] [code] [blog]
This work formally defines the problem of open long-tailed recognition (OLTR) as optimizing for the overall accuracy of a naturally-distributed dataset with the presence of open classes.
Insights and Approaches Using Deep Learning to Classify Wildlife
Zhongqi Miao, Kaitlyn M Gaynor, Jiayun Wang, Ziwei Liu, Oliver Muellerklein, Mohammad S. Norouzzadeh, Alex McInturff, Rauri C. K. Bowie, Ran Nathon, Stella X. Yu, Wayne M. Getz.
Nature - Scientific Reports 2019. [Paper]
This work aims to interpret the concepts behind the convolutional neural networks (CNNs) in classifying wildlife.
Deep Ranking Model by Large Adaptive Margin Learning for Person Re-identification
Jiayun Wang, Sanping Zhou, Jinjun Wang, Qiqi Hou.
Pattern Recognition 2018.
[paper] [arxiv] [code]
This work designs a dynamically adaptive loss function to overcome the drawbacks of conventional loss functions for person re-identification.
Successive Embedding and Classification Loss for Aerial Image Classification
Jiayun Wang, Patrick Virtue, Stella X. Yu.
arXiv 2017. [paper] [code]
This work aims to address the overfitting problem in remote sensing image classification.
Point to Set Similarity Based Deep Feature Learning for Person Re-identification
Sanping Zhou, Jinjun Wang, Jiayun Wang, Yihong Gong, Nanning Zheng.
CVPR 2017.
[paper] [code]
This work presents a novel metric learning method based on P2S similarity comparison for person re-identification.
At Berkeley, I taught the following classes as a graduate student instructor:
CS182/282, Deep Learning, Spring 2023
CS189/289, Machine Learning, Fall 2020
VS205, Visual Perception, Fall 2019, 2018
I gave talks at seminars of Apple, Amazon, Bosch, Oxyopia, etc. You could watch some of the conferencepresentations of my works on YouTube:
Towards Real-World Structure-Aware Representation Learning, 2023
Compact and Optimal Deep Learning with Recurrent Parameter Generators, 2022
Spatial Transformer for 3D Point Clouds, 2021
Orthogonal Convolutional Neural Networks, 2020