1 code implementation • 1 Apr 2024 • Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang, Kyung-Ah Sohn
Experimental results indicate that while pre-trained models and those fine-tuned with a chit-chat dataset struggle to generate responses reflecting personality, models trained with PSYDIAL show significant improvements.
1 code implementation • COLING 2022 • Jiyun Kim, Byounghan Lee, Kyung-Ah Sohn
In a hate speech detection model, we should consider two critical aspects in addition to detection performance-bias and explainability.
Ranked #1 on Hate Speech Detection on HateXplain
no code implementations • 29 Sep 2021 • Jeong-Hyeon Moon, Namhyuk Ahn, Kyung-Ah Sohn
Out-of-distribution (OOD) detection has made significant progress in recent years because the distribution mismatch between the training and testing can severely deteriorate the reliability of a machine learning system. Nevertheless, the lack of precise interpretation of the in-distribution limits the application of OOD detection methods to real-world system pipielines.
Out-of-Distribution Detection Out of Distribution (OOD) Detection +1
no code implementations • 29 Sep 2021 • Jangwook Kim, Kyung-Ah Sohn
Once the features are extracted from an image data, the bias prediction network tries to recover the bias of the raw image such as color from the features.
1 code implementation • 20 Apr 2021 • JuneKyu Park, Jeong-Hyeon Moon, Namhyuk Ahn, Kyung-Ah Sohn
From a safety perspective, a machine learning method embedded in real-world applications is required to distinguish irregular situations.
1 code implementation • COLING 2020 • Heejin Kim, Kyung-Ah Sohn
In both approaches, however, it is impossible to adjust the strength of the style in the generated output.
1 code implementation • 30 Sep 2020 • Sijin Kim, Namhyuk Ahn, Kyung-Ah Sohn
Viewing in a different point of combining, we introduce a spatially-heterogeneous distortion dataset in which multiple corruptions are applied to the different locations of each image.
5 code implementations • 5 May 2020 • Andreas Lugmayr, Martin Danelljan, Radu Timofte, Namhyuk Ahn, Dongwoon Bai, Jie Cai, Yun Cao, Junyang Chen, Kaihua Cheng, SeYoung Chun, Wei Deng, Mostafa El-Khamy, Chiu Man Ho, Xiaozhong Ji, Amin Kheradmand, Gwantae Kim, Hanseok Ko, Kanghyu Lee, Jungwon Lee, Hao Li, Ziluan Liu, Zhi-Song Liu, Shuai Liu, Yunhua Lu, Zibo Meng, Pablo Navarrete Michelini, Christian Micheloni, Kalpesh Prajapati, Haoyu Ren, Yong Hyeok Seo, Wan-Chi Siu, Kyung-Ah Sohn, Ying Tai, Rao Muhammad Umer, Shuangquan Wang, Huibing Wang, Timothy Haoning Wu, Hao-Ning Wu, Biao Yang, Fuzhi Yang, Jaejun Yoo, Tongtong Zhao, Yuanbo Zhou, Haijie Zhuo, Ziyao Zong, Xueyi Zou
This paper reviews the NTIRE 2020 challenge on real world super-resolution.
no code implementations • 23 Apr 2020 • Namhyuk Ahn, Jaejun Yoo, Kyung-Ah Sohn
In this paper, we tackle a fully unsupervised super-resolution problem, i. e., neither paired images nor ground truth HR images.
2 code implementations • CVPR 2020 • Jaejun Yoo, Namhyuk Ahn, Kyung-Ah Sohn
The key intuition of CutBlur is to enable a model to learn not only "how" but also "where" to super-resolve an image.
1 code implementation • 6 Mar 2019 • Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Recent progress in deep learning-based models has improved photo-realistic (or perceptual) single-image super-resolution significantly.
no code implementations • 28 May 2018 • Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
Image distortion classification and detection is an important task in many applications.
3 code implementations • ECCV 2018 • Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn
In recent years, deep learning methods have been successfully applied to single-image super-resolution tasks.
Ranked #17 on Image Super-Resolution on BSD100 - 2x upscaling
no code implementations • 23 Oct 2017 • Jonghwa Yim, Kyung-Ah Sohn
Therefore, in this study, we exhaustively research skip connections of state-of-the-art deep convolutional networks and investigate the characteristics of the features from each intermediate layer.
no code implementations • 18 Oct 2017 • Jonghwa Yim, Kyung-Ah Sohn
Despite the appeal of deep neural networks that largely replace the traditional handmade filters, they still suffer from isolated cases that cannot be properly handled only by the training of convolutional filters.