The impact of customizable instructions on the outcome of a training process is demonstrated on a state-of-the-art reinforcement learning environment.
no code implementations • 6 May 2020 • Shanxin Yuan, Radu Timofte, Ales Leonardis, Gregory Slabaugh, Xiaotong Luo, Jiangtao Zhang, Yanyun Qu, Ming Hong, Yuan Xie, Cuihua Li, Dejia Xu, Yihao Chu, Qingyan Sun, Shuai Liu, Ziyao Zong, Nan Nan, Chenghua Li, Sangmin Kim, Hyungjoon Nam, Jisu Kim, Jechang Jeong, Manri Cheon, Sung-Jun Yoon, Byungyeon Kang, Junwoo Lee, Bolun Zheng, Xiaohong Liu, Linhui Dai, Jun Chen, Xi Cheng, Zhen-Yong Fu, Jian Yang, Chul Lee, An Gia Vien, Hyunkook Park, Sabari Nathan, M. Parisa Beham, S Mohamed Mansoor Roomi, Florian Lemarchand, Maxime Pelcat, Erwan Nogues, Densen Puthussery, Hrishikesh P. S, Jiji C. V, Ashish Sinha, Xuan Zhao
Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image.
When the target noise is complex, e. g. composed of an unknown mixture of primary noises with unknown intensity, fully supervised solutions are limited by the difficulty to build a suited training set for the problem.
Image denoising has recently taken a leap forward due to machine learning.
This paper proposes an upgraded electro-magnetic side-channel attack that automatically reconstructs the intercepted data.
Convolutional Neural Networks (CNNs) are computationally intensive algorithms that currently require dedicated hardware to be executed.
Distributed, Parallel, and Cluster Computing Hardware Architecture
Deep Convolutional Neural Networks (CNNs) are the state-of-the-art in image classification.
Deep Convolutional Neural Networks (CNNs) are the state of the art systems for image classification and scene understating.
Other Computer Science
Deep Neural Networks are becoming the de-facto standard models for image understanding, and more generally for computer vision tasks.