| Learning structured output representation using deep conditional generative models K Sohn, H Lee, X Yan Advances in neural information processing systems 28, 3483-3491, 2015 | 1084 | 2015 |
| Improved deep metric learning with multi-class n-pair loss objective K Sohn Advances in neural information processing systems 29, 1857-1865, 2016 | 598 | 2016 |
| Attribute2image: Conditional image generation from visual attributes X Yan, J Yang, K Sohn, H Lee European Conference on Computer Vision, 776-791, 2016 | 580 | 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 | 409 | 2018 |
| 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 | 328 | 2016 |
| 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 | 204 | 2014 |
| 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 | 198 | 2017 |
| 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 | 187 | 2013 |
| 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 | 172 | 2015 |
| Learning invariant representations with local transformations K Sohn, H Lee international conference on machine learning, 2012 | 164 | 2012 |
| Online incremental feature learning with denoising autoencoders G Zhou, K Sohn, H Lee Artificial intelligence and statistics, 1453-1461, 2012 | 162 | 2012 |
| Improved multimodal deep learning with variation of information K Sohn, W Shang, H Lee Advances in neural information processing systems 27, 2141-2149, 2014 | 155 | 2014 |
| 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 | 146 | 2011 |
| Fixmatch: Simplifying semi-supervised learning with consistency and confidence K Sohn, D Berthelot, CL Li, Z Zhang, N Carlini, ED Cubuk, A Kurakin, ... arXiv preprint arXiv:2001.07685, 2020 | 138 | 2020 |
| 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 | 101 | 2017 |
| Learning and selecting features jointly with point-wise gated {B} oltzmann machines K Sohn, G Zhou, C Lee, H Lee Proceedings of The 30th International Conference on Machine Learning, 217-225, 2013 | 101 | 2013 |
| Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring D Berthelot, N Carlini, ED Cubuk, A Kurakin, K Sohn, H Zhang, C Raffel International Conference on Learning Representations, 2019 | 99* | 2019 |
| Feature transfer learning for face recognition with under-represented data X Yin, X Yu, K Sohn, X Liu, M Chandraker Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019 | 86* | 2019 |
| Unsupervised domain adaptation for face recognition in unlabeled videos K Sohn, S Liu, G Zhong, X Yu, MH Yang, M Chandraker Proceedings of the IEEE International Conference on Computer Vision, 3210-3218, 2017 | 78 | 2017 |
| Domain adaptation for structured output via discriminative patch representations YH Tsai, K Sohn, S Schulter, M Chandraker Proceedings of the IEEE International Conference on Computer Vision, 1456-1465, 2019 | 67 | 2019 |
Honglak LeeLG AI Research / U. MichiganVerified email at umich.edu
