120 citations · 357 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…
Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
Yufei Wang, Tianwei Ni
Exploration-exploitation dilemma has long been a crucial issue in reinforcement learning. In this paper, we propose a new approach to automatically balance between these two. Our m…
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
Ziju Shen, Yufei Wang, Dufan Wu +2
Computed Tomography (CT) takes X-ray measurements on the subjects to reconstruct tomographic images. As X-ray is radioactive, it is desirable to control the total amount of dose of…