6 citations · 9 across the 3 of their papers we have counts for
4 papers
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results
Andreas Lugmayr, Martin Danelljan, Radu Timofte +18
This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting,…
Frequency Separation for Real-World Super-Resolution
Manuel Fritsche, Shuhang Gu, Radu Timofte
Most of the recent literature on image super-resolution (SR) assumes the availability of training data in the form of paired low resolution (LR) and high resolution (HR) images or…
Efficient Smoothing of Dilated Convolutions for Image Segmentation
Thomas Ziegler, Manuel Fritsche, Lorenz Kuhn +1
Dilated Convolutions have been shown to be highly useful for the task of image segmentation. By introducing gaps into convolutional filters, they enable the use of larger receptive…
Using State Predictions for Value Regularization in Curiosity Driven Deep Reinforcement Learning
Gino Brunner, Manuel Fritsche, Oliver Richter +1
Learning in sparse reward settings remains a challenge in Reinforcement Learning, which is often addressed by using intrinsic rewards. One promising strategy is inspired by human c…