| Learning structured output representation using deep conditional generative models K Sohn, H Lee, X Yan Advances in neural information processing systems 28, 2015 | 2416 | 2015 |
| 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 | 1689 | 2020 |
| Improved deep metric learning with multi-class n-pair loss objective K Sohn Advances in neural information processing systems, 1857-1865, 2016 | 1637 | 2016 |
| 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 | 1177 | 2018 |
| 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 | 780 | 2016 |
| 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 | 715 | 2019 |
| 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 | 513 | 2016 |
| 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 | 344 | 2017 |
| 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 | 267* | 2019 |
| 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 | 264 | 2014 |
| 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 | 253 | 2021 |
| 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 | 252 | 2019 |
| 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 | 247 | 2020 |
| 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 | 236 | 2015 |
| Online incremental feature learning with denoising autoencoders G Zhou, K Sohn, H Lee Artificial intelligence and statistics, 1453-1461, 2012 | 228 | 2012 |
| 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 | 214 | 2013 |
| Learning invariant representations with local transformations K Sohn, H Lee international conference on machine learning, 2012 | 213 | 2012 |
| Improved multimodal deep learning with variation of information K Sohn, W Shang, H Lee Advances in neural information processing systems 27, 2014 | 202 | 2014 |
| Reconstruction-based disentanglement for pose-invariant face recognition X Peng, X Yu, K Sohn, DN Metaxas, M Chandraker Proceedings of the IEEE international conference on computer vision, 1623-1632, 2017 | 164 | 2017 |
| Efficient learning of sparse, distributed, convolutional feature representations for object recognition K Sohn, DY Jung, H Lee, AO Hero 2011 International Conference on Computer Vision, 2643-2650, 2011 | 162 | 2011 |
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