1 code implementation • 17 May 2021 • Andrey Ignatov, Cheng-Ming Chiang, Hsien-Kai Kuo, Anastasia Sycheva, Radu Timofte, Min-Hung Chen, Man-Yu Lee, Yu-Syuan Xu, Yu Tseng, Shusong Xu, Jin Guo, Chao-Hung Chen, Ming-Chun Hsyu, Wen-Chia Tsai, Chao-Wei Chen, Grigory Malivenko, Minsu Kwon, Myungje Lee, Jaeyoon Yoo, Changbeom Kang, Shinjo Wang, Zheng Shaolong, Hao Dejun, Xie Fen, Feng Zhuang, Yipeng Ma, Jingyang Peng, Tao Wang, Fenglong Song, Chih-Chung Hsu, Kwan-Lin Chen, Mei-Hsuang Wu, Vishal Chudasama, Kalpesh Prajapati, Heena Patel, Anjali Sarvaiya, Kishor Upla, Kiran Raja, Raghavendra Ramachandra, Christoph Busch, Etienne de Stoutz
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual aspects of mobile photos.
no code implementations • 17 May 2021 • Andrey Ignatov, Kim Byeoung-su, Radu Timofte, Angeline Pouget, Fenglong Song, Cheng Li, Shuai Xiao, Zhongqian Fu, Matteo Maggioni, Yibin Huang, Shen Cheng, Xin Lu, Yifeng Zhou, Liangyu Chen, Donghao Liu, Xiangyu Zhang, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Minsu Kwon, Myungje Lee, Jaeyoon Yoo, Changbeom Kang, Shinjo Wang, Bin Huang, Tianbao Zhou, Shuai Liu, Lei Lei, Chaoyu Feng, Liguang Huang, Zhikun Lei, Feifei Chen
A detailed description of all models developed in the challenge is provided in this paper.
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.
We propose a novel domain adaptation method based on label propagation and cycle consistency to let the clusters of the features from the two domains overlap exactly and become clear for high accuracy.
The objective of this study is to train an autonomous navigation model that uses a simulator (instead of real labeled data) and an inexpensive monocular camera.
Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution.
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user.
Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy.