| Uncertainty-based continual learning with adaptive regularization H Ahn, S Cha, D Lee, T Moon Advances in Neural Information Processing Systems (NeurIPS) 2019, 2019 | 181 | 2019 |
| Toward a unified framework for interpreting machine-learning models in neuroimaging L Kohoutová, J Heo, S Cha, S Lee, T Moon, TD Wager, CW Woo Nature protocols 15 (4), 1399-1435, 2020 | 112 | 2020 |
| Continual Learning with Node-Importance based Adaptive Group Sparse Regularization S Jung, H Ahn, S Cha, T Moon Advances in Neural Information Processing Systems (NeurIPS) 2020, 2020 | 101 | 2020 |
| CPR: Classifier-Projection Regularization for Continual Learning S Cha, H Hsu, T Hwang, FP Calmon, T Moon International Conference on Learning Representations (ICLR) 2021, 2021 | 62 | 2021 |
| SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning S Cha, B Kim, Y Yoo, T Moon Advances in Neural Information Processing Systems (NeurIPS) 2021, 2021 | 58 | 2021 |
| Fully convolutional pixel adaptive image denoiser S Cha, T Moon International Conference on Computer Vision (ICCV) 2019, 4160-4169, 2019 | 54 | 2019 |
| FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise J Byun, S Cha, T Moon Conference on Computer Vision and Pattern Recognition (CVPR) 2021, 5768-5777, 2021 | 49 | 2021 |
| Knowledge unlearning for mitigating privacy risks in language models J Jang, D Yoon, S Yang, S Cha, M Lee, L Logeswaran, M Seo arXiv preprint arXiv:2210.01504, 2022 | 47 | 2022 |
| GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images S Cha, T Park, B Kim, J Baek, T Moon International Conference on Learning Representations (ICLR) 2021, 2021 | 42* | 2021 |
| Neural adaptive image denoiser S Cha, T Moon International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2018 | 26 | 2018 |
| Rebalancing batch normalization for exemplar-based class-incremental learning S Cha, S Cho, D Hwang, S Hong, M Lee, T Moon Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 11* | 2023 |
| Learning to unlearn: Instance-wise unlearning for pre-trained classifiers S Cha, S Cho, D Hwang, H Lee, T Moon, M Lee The 38th Annual AAAI Conference on Artificial Intelligence 2024, 2023 | 7 | 2023 |
| Towards More Objective Evaluation of Class Incremental Learning: Representation Learning Perspective S Cha, J Kwak, D Shim, H Kim, M Lee, H Lee, T Moon arXiv preprint arXiv:2206.08101, 2022 | 5* | 2022 |
| Observations on K-Image Expansion of Image-Mixing Augmentation J Jeong, S Cha, J Choi, S Yun, T Moon, Y Yoo IEEE Access 11, 16631-16643, 2023 | 4* | 2023 |
| DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling S Joo, S Cha, T Moon Thirty-Third AAAI Conference on Artificial Intelligence 2019, 2019 | 3 | 2019 |
| Udlr convolutional network for adaptive image denoiser S Cha, T Moon Robot Intelligence Technology and Applications: 6th International Conference …, 2019 | 3 | 2019 |
| NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations S Cha, N Ko, H Choi, Y Yoo, T Moon Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 1 | 2024 |
| Hyperparameters in Continual Learning: a Reality Check S Cha, K Cho arXiv preprint arXiv:2403.09066, 2024 | | 2024 |
| Augmenting Negative Representation for Continual Self-Supervised Learning S Cha, K Cho, T Moon | | 2023 |
| Supervised Neural Discrete Universal Denoiser for Adaptive Denoising S Cha, S Min, S Yoon, T Moon arXiv preprint arXiv:2111.12350, 2021 | | 2021 |
Taesup Moon (문태섭)Associate Professor, Department of Electrical and Computer Engineering, Seoul National Universitysnu.ac.kr의 이메일 확인됨
