380 citations · 419 across the 8 of their papers we have counts for
8 papers
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Kim Byeoung-su, Radu Timofte +29
Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data…
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Cheng-Ming Chiang, Hsien-Kai Kuo +38
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…
Joint Contrastive Learning for Unsupervised Domain Adaptation
Changhwa Park, Jonghyun Lee, Jaeyoon Yoo +2
Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination. In par…
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation
Jaeyoon Yoo, Changhwa Park, Yongjun Hong +1
Unsupervised domain adaptation aiming to learn a specific task for one domain using another domain data has emerged to address the labeling issue in supervised learning, especially…
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
Jaeyoon Yoo, Yongjun Hong, YungKyun Noh +1
Autonomous navigation has become an increasingly popular machine learning application. Recent advances in deep learning have also resulted in great improvements to autonomous navig…
How Generative Adversarial Networks and Their Variants Work: An Overview
Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo +1
Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution. Spec…
Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning
Jaeyoon Yoo, Heonseok Ha, Jihun Yi +5
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. Towards this goal, energy-…
Training IBM Watson using Automatically Generated Question-Answer Pairs
Jangho Lee, Gyuwan Kim, Jaeyoon Yoo +3
IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant quest…