| A simple unified framework for detecting out-of-distribution samples and adversarial attacks K Lee, K Lee, H Lee, J Shin Advances in neural information processing systems 31, 2018 | 2935 | 2018 |
| Training confidence-calibrated classifiers for detecting out-of-distribution samples K Lee, H Lee, K Lee, J Shin arXiv preprint arXiv:1711.09325, 2017 | 1179 | 2017 |
| Csi: Novelty detection via contrastive learning on distributionally shifted instances J Tack, S Mo, J Jeong, J Shin Advances in neural information processing systems 33, 11839-11852, 2020 | 867 | 2020 |
| Learning from failure: De-biasing classifier from biased classifier J Nam, H Cha, S Ahn, J Lee, J Shin Advances in Neural Information Processing Systems 33, 20673-20684, 2020 | 603 | 2020 |
| Co2l: Contrastive continual learning H Cha, J Lee, J Shin Proceedings of the IEEE/CVF International conference on computer vision …, 2021 | 526 | 2021 |
| Regularizing class-wise predictions via self-knowledge distillation S Yun, J Park, K Lee, J Shin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 432 | 2020 |
| Layer-adaptive sparsity for the magnitude-based pruning J Lee, S Park, S Mo, S Ahn, J Shin arXiv preprint arXiv:2010.07611, 2020 | 404 | 2020 |
| M2m: Imbalanced classification via major-to-minor translation J Kim, J Jeong, J Shin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 350 | 2020 |
| Overcoming catastrophic forgetting with unlabeled data in the wild K Lee, K Lee, J Shin, H Lee Proceedings of the IEEE/CVF international conference on computer vision, 312-321, 2019 | 330 | 2019 |
| Offline-to-online reinforcement learning via balanced replay and pessimistic q-ensemble S Lee, Y Seo, K Lee, P Abbeel, J Shin Conference on Robot Learning, 1702-1712, 2022 | 314 | 2022 |
| Network adiabatic theorem: an efficient randomized protocol for contention resolution S Rajagopalan, D Shah, J Shin ACM SIGMETRICS performance evaluation review 37 (1), 133-144, 2009 | 298 | 2009 |
| Representation alignment for generation: Training diffusion transformers is easier than you think S Yu, S Kwak, H Jang, J Jeong, J Huang, J Shin, S Xie arXiv preprint arXiv:2410.06940, 2024 | 292 | 2024 |
| Freeze the discriminator: a simple baseline for fine-tuning gans S Mo, M Cho, J Shin arXiv preprint arXiv:2002.10964, 2020 | 285 | 2020 |
| Neural adaptive content-aware internet video delivery H Yeo, Y Jung, J Kim, J Shin, D Han 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2018 | 279 | 2018 |
| Network randomization: A simple technique for generalization in deep reinforcement learning K Lee, K Lee, J Shin, H Lee arXiv preprint arXiv:1910.05396, 2019 | 273 | 2019 |
| Video probabilistic diffusion models in projected latent space S Yu, K Sohn, S Kim, J Shin Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 264 | 2023 |
| Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning J Kim, Y Hur, S Park, E Yang, SJ Hwang, J Shin Advances in neural information processing systems 33, 14567-14579, 2020 | 257 | 2020 |
| Self-supervised label augmentation via input transformations H Lee, SJ Hwang, J Shin International Conference on Machine Learning, 5714-5724, 2020 | 256 | 2020 |
| Generating videos with dynamics-aware implicit generative adversarial networks S Yu, J Tack, S Mo, H Kim, J Kim, JW Ha, J Shin arXiv preprint arXiv:2202.10571, 2022 | 254 | 2022 |
| Instagan: Instance-aware image-to-image translation S Mo, M Cho, J Shin arXiv preprint arXiv:1812.10889, 2018 | 222 | 2018 |
