59 citations · 64 across the 5 of their papers we have counts for
5 papers
Reply to: Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture
Changjun Fan, Mutian Shen, Zohar Nussinov +3
We wish to thank Stefan Boettcher for prompting us to further check and highlight the accuracy and scaling of our results. Here we provide a comprehensive response to the Comment w…
URCDC-Depth: Uncertainty Rectified Cross-Distillation with CutFlip for Monocular Depth Estimation
Shuwei Shao, Zhongcai Pei, Weihai Chen +3
This work aims to estimate a high-quality depth map from a single RGB image. Due to the lack of depth clues, making full use of the long-range correlation and the local information…
Self-Supervised Monocular Depth Estimation with Self-Reference Distillation and Disparity Offset Refinement
Zhong Liu, Ran Li, Shuwei Shao +2
Monocular depth estimation plays a fundamental role in computer vision. Due to the costly acquisition of depth ground truth, self-supervised methods that leverage adjacent frames t…
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks
Ziyang Liu, Zhengguo Li, Xingming Wu +2
The generative adversarial network (GAN) is successfully applied to study the perceptual single image superresolution (SISR). However, the GAN often tends to generate images with h…
PTR-PPO: Proximal Policy Optimization with Prioritized Trajectory Replay
Xingxing Liang, Yang Ma, Yanghe Feng +1
On-policy deep reinforcement learning algorithms have low data utilization and require significant experience for policy improvement. This paper proposes a proximal policy optimiza…