| A simple unified framework for detecting out-of-distribution samples and adversarial attacks K Lee, K Lee, H Lee, J Shin Conference on Neural Information Processing Systems, 2018 | 2944 | 2018 |
| Training confidence-calibrated classifiers for detecting out-of-distribution samples K Lee, H Lee, K Lee, J Shin International Conference on Learning Representations, 2018 | 1184 | 2018 |
| Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild K Lee, K Lee, J Shin, H Lee Proceedings of the IEEE International Conference on Computer Vision, 312-321, 2019 | 330 | 2019 |
| Network randomization: A simple technique for generalization in deep reinforcement learning K Lee, K Lee, J Shin, H Lee International Conference on Learning Representations, 2020 | 273 | 2020 |
| Robust inference via generative classifiers for handling noisy labels K Lee, S Yun, K Lee, H Lee, B Li, J Shin International Conference on Machine Learning, 2019 | 194 | 2019 |
| i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning K Lee, Y Zhu, K Sohn, CL Li, J Shin, H Lee International Conference on Learning Representations, 2021 | 187* | 2021 |
| Augmenting supervised neural networks with unsupervised objectives for large-scale image classification Y Zhang, K Lee, H Lee International conference on machine learning, 612-621, 2016 | 184 | 2016 |
| Hierarchical novelty detection for visual object recognition K Lee, K Lee, K Min, Y Zhang, J Shin, H Lee Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 105 | 2018 |
| Towards understanding the invertibility of convolutional neural networks AC Gilbert, Y Zhang, K Lee, Y Zhang, H Lee International Joint Conferences on Artificial Intelligence, 2017 | 86 | 2017 |
| Learning to embed time series patches independently S Lee, T Park, K Lee arXiv preprint arXiv:2312.16427, 2023 | 71 | 2023 |
| Improving transferability of representations via augmentation-aware self-supervision H Lee, K Lee, K Lee, H Lee, J Shin Advances in Neural Information Processing Systems 34, 17710-17722, 2021 | 66 | 2021 |
| Soft contrastive learning for time series S Lee, T Park, K Lee arXiv preprint arXiv:2312.16424, 2023 | 65 | 2023 |
| Shapeadv: Generating shape-aware adversarial 3d point clouds K Lee, Z Chen, X Yan, R Urtasun, E Yumer arXiv preprint arXiv:2005.11626, 2020 | 50 | 2020 |
| A flexible framework for online document segmentation by pairwise stroke distance learning A Delaye, K Lee Pattern Recognition 48 (4), 1197-1210, 2015 | 31 | 2015 |
| Rethinking few-shot object detection on a multi-domain benchmark K Lee, H Yang, S Chakraborty, Z Cai, G Swaminathan, A Ravichandran, ... European Conference on Computer Vision, 366-382, 2022 | 26 | 2022 |
| Automatic correction of lithography hotspots with a deep generative model W Sim, K Lee, D Yang, J Jeong, JS Hong, S Lee, H Lee Optical Microlithography XXXII 10961, 1096105, 2019 | 21 | 2019 |
| Device and method of providing handwritten content in the same KB Lee, A DELAYE US Patent 10,649,647, 2020 | 19 | 2020 |
| On the equivalence of linear discriminant analysis and least squares K Lee, J Kim Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 13 | 2015 |
| Ant: Adaptive noise schedule for time series diffusion models S Lee, K Lee, T Park Advances in Neural Information Processing Systems 37, 121099-121130, 2024 | 8 | 2024 |
| To Predict or Not to Predict? Proportionally Masked Autoencoders for Tabular Data Imputation J Kim, K Lee, T Park Proceedings of the AAAI Conference on Artificial Intelligence 39 (17), 17886 …, 2025 | 4 | 2025 |
Kimin LeeKAISTVerified email at kaist.ac.kr
