| Rethinking the truly unsupervised image-to-image translation K Baek, Y Choi, Y Uh, J Yoo, H Shim Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 111 | 2021 |
| Psynet: Self-supervised approach to object localization using point symmetric transformation K Baek, M Lee, H Shim Proceedings of the AAAI Conference on Artificial Intelligence 34 (07), 10451 …, 2020 | 38 | 2020 |
| GridMix: Strong regularization through local context mapping K Baek, D Bang, H Shim Pattern Recognition 109, 107594, 2021 | 25 | 2021 |
| Commonality in natural images rescues GANs: Pretraining GANs with generic and privacy-free synthetic data K Baek, H Shim Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 8 | 2022 |
| Editable generative adversarial networks: Generating and editing faces simultaneously K Baek, D Bang, H Shim Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 7 | 2019 |
| Learning from better supervision: Self-distillation for learning with noisy labels K Baek, S Lee, H Shim 2022 26th International Conference on Pattern Recognition (ICPR), 1829-1835, 2022 | 3 | 2022 |
| Logit Mixing Training for More Reliable and Accurate Prediction. D Bang, K Baek, J Kim, Y Jeon, JH Kim, J Kim, J Lee, H Shim IJCAI, 2812-2819, 2022 | 3 | 2022 |
| Generic and Privacy-free Synthetic Data Generation for Pretraining GANs K Baek, H Shim NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research, 2022 | | 2022 |
Hyunjung ShimAssociate Professor, KAISTBestätigte E-Mail-Adresse bei kaist.ac.kr
