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Kihyuk Sohn
Kihyuk Sohn
Research Scientist, Meta Reality Labs
Verified email at meta.com - Homepage
Title
Cited by
Cited by
Year
Learning structured output representation using deep conditional generative models
K Sohn, H Lee, X Yan
Advances in neural information processing systems 28, 2015
33892015
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
K Sohn, D Berthelot, N Carlini, Z Zhang, H Zhang, CA Raffel, ED Cubuk, ...
Advances in neural information processing systems 33, 596-608, 2020
32622020
Improved deep metric learning with multi-class n-pair loss objective
K Sohn
Advances in neural information processing systems, 1857-1865, 2016
23482016
Learning to adapt structured output space for semantic segmentation
YH Tsai, WC Hung, S Schulter, K Sohn, MH Yang, M Chandraker
Proceedings of the IEEE conference on computer vision and pattern …, 2018
16532018
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
D Berthelot, N Carlini, ED Cubuk, A Kurakin, K Sohn, H Zhang, C Raffel
arXiv preprint arXiv:1911.09785, 2019
11302019
Attribute2image: Conditional image generation from visual attributes
X Yan, J Yang, K Sohn, H Lee
Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016
8602016
Cutpaste: Self-supervised learning for anomaly detection and localization
CL Li, K Sohn, J Yoon, T Pfister
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
6962021
Understanding and improving convolutional neural networks via concatenated rectified linear units
W Shang, K Sohn, D Almeida, H Lee
international conference on machine learning, 2217-2225, 2016
6082016
A simple semi-supervised learning framework for object detection
K Sohn, Z Zhang, CL Li, H Zhang, CY Lee, T Pfister
arXiv preprint arXiv:2005.04757, 2020
4722020
Towards large-pose face frontalization in the wild
X Yin, X Yu, K Sohn, X Liu, M Chandraker
Proceedings of the IEEE international conference on computer vision, 3990-3999, 2017
4032017
Domain adaptation for structured output via discriminative patch representations
YH Tsai, K Sohn, S Schulter, M Chandraker
Proceedings of the IEEE/CVF international conference on computer vision …, 2019
3682019
Feature transfer learning for face recognition with under-represented data
X Yin, X Yu, K Sohn, X Liu, M Chandraker
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
3542019
Learning to disentangle factors of variation with manifold interaction
S Reed, K Sohn, Y Zhang, H Lee
International conference on machine learning, 1431-1439, 2014
2892014
Online incremental feature learning with denoising autoencoders
G Zhou, K Sohn, H Lee
Artificial intelligence and statistics, 1453-1461, 2012
2732012
Crest: A class-rebalancing self-training framework for imbalanced semi-supervised learning
C Wei, K Sohn, C Mellina, A Yuille, F Yang
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021
2662021
Improving object detection with deep convolutional networks via bayesian optimization and structured prediction
Y Zhang, K Sohn, R Villegas, G Pan, H Lee
Proceedings of the IEEE conference on computer vision and pattern …, 2015
2662015
Learning invariant representations with local transformations
K Sohn, H Lee
international conference on machine learning, 2012
2372012
Augmenting CRFs with Boltzmann machine shape priors for image labeling
A Kae, K Sohn, H Lee, E Learned-Miller
Proceedings of the IEEE conference on computer vision and pattern …, 2013
2292013
Learning and evaluating representations for deep one-class classification
K Sohn, CL Li, J Yoon, M Jin, T Pfister
arXiv preprint arXiv:2011.02578, 2020
2212020
Improved multimodal deep learning with variation of information
K Sohn, W Shang, H Lee
Advances in neural information processing systems 27, 2014
2172014
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