Search Results for author: Jaeyoon Yoo

Found 8 papers, 2 papers with code

Joint Contrastive Learning for Unsupervised Domain Adaptation

1 code implementation18 Jun 2020 Changhwa Park, Jonghyun Lee, Jaeyoon Yoo, Minhoe Hur, Sungroh Yoon

Enhancing feature transferability by matching marginal distributions has led to improvements in domain adaptation, although this is at the expense of feature discrimination.

Contrastive Learning Unsupervised Domain Adaptation

Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation

no code implementations12 Mar 2019 Jaeyoon Yoo, Changhwa Park, Yongjun Hong, Sungroh Yoon

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.

Association Unsupervised Domain Adaptation

Domain Adaptation Using Adversarial Learning for Autonomous Navigation

no code implementations11 Dec 2017 Jaeyoon Yoo, Yongjun Hong, Yung-Kyun Noh, Sungroh Yoon

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.

Autonomous Navigation Domain Adaptation +1

How Generative Adversarial Networks and Their Variants Work: An Overview

no code implementations16 Nov 2017 Yongjun Hong, Uiwon Hwang, Jaeyoon Yoo, Sungroh Yoon

Generative Adversarial Networks (GAN) have received wide attention in the machine learning field for their potential to learn high-dimensional, complex real data distribution.

Domain Adaptation Image Generation +1

Energy-Based Sequence GANs for Recommendation and Their Connection to Imitation Learning

no code implementations28 Jun 2017 Jaeyoon Yoo, Heonseok Ha, Jihun Yi, Jongha Ryu, Chanju Kim, Jung-Woo Ha, Young-Han Kim, Sungroh Yoon

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.

Imitation Learning Recommendation Systems +1

Training IBM Watson using Automatically Generated Question-Answer Pairs

no code implementations12 Nov 2016 Jangho Lee, Gyuwan Kim, Jaeyoon Yoo, Changwoo Jung, Minseok Kim, Sungroh Yoon

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.

Answer Generation Question-Answer-Generation +1

Cannot find the paper you are looking for? You can Submit a new open access paper.