activity
20172020
most citedHeterogeneous Domain Generalization via Domain Mixup

120 citations · 316 across the 10 of their papers we have counts for

collaborators

11 papers

cs.LG20204 cited

f-IRL: Inverse Reinforcement Learning via State Marginal Matching

Tianwei Ni, Harshit Sikchi, Yufei Wang +3

Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method…

cs.LG20208 cited

ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

Yufei Wang, Gautham Narayan Narasimhan, Xingyu Lin +2

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling a…

cs.RO202068 cited

SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation

Xingyu Lin, Yufei Wang, Jake Olkin +1

Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement le…

cs.CV202082 cited

Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization

Haoliang Li, YuFei Wang, Renjie Wan +3

Recently, we have witnessed great progress in the field of medical imaging classification by adopting deep neural networks. However, the recent advanced models still require access…

cs.CR202013 cited

Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems

Haoliang Li, Yufei Wang, Xiaofei Xie +5

Deep neural networks (DNN) have shown great success in many computer vision applications. However, they are also known to be susceptible to backdoor attacks. When conducting backdo…

cs.CV2020120 cited

Heterogeneous Domain Generalization via Domain Mixup

Yufei Wang, Haoliang Li, Alex C. Kot

One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain ge…