| Uncertainty-based continual learning with adaptive regularization H Ahn, S Cha, D Lee, T Moon Advances in Neural Information Processing Systems (NeurIPS), 2019 | 246 | 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, 2020 | 139 | 2020 |
| Knowledge unlearning for mitigating privacy risks in language models J Jang, D Yoon, S Yang, S Cha, M Lee, L Logeswaran, M Seo Association For Computational Linguistics (ACL), 2023 | 136 | 2023 |
| 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 | 125 | 2020 |
| 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 | 87 | 2021 |
| CPR: Classifier-Projection Regularization for Continual Learning S Cha, H Hsu, T Hwang, FP Calmon, T Moon International Conference on Learning Representations (ICLR), 2021 | 76 | 2021 |
| FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise J Byun, S Cha, T Moon IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 | 58 | 2021 |
| Fully convolutional pixel adaptive image denoiser S Cha, T Moon International Conference on Computer Vision (ICCV), 2019 | 57 | 2019 |
| 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 | 53* | 2021 |
| Neural adaptive image denoiser S Cha, T Moon International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2018 | 26 | 2018 |
| Learning to unlearn: Instance-wise unlearning for pre-trained classifiers S Cha, S Cho, D Hwang, H Lee, T Moon, M Lee AAAI Conference on Artificial Intelligence, 2024 | 23 | 2024 |
| Rebalancing Batch Normalization for Exemplar-based Class-incremental Learning S Cha, S Cho, D Hwang, S Hong, M Lee, T Moon IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 | 22* | 2023 |
| Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training H Choi, H Park, KM Yi, S Cha, D Min European Conference on Computer Vision (ECCV), 2024 | 7 | 2024 |
| Towards More Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective S Cha, J Kwak, D Shim, H Kim, M Lee, H Lee, T Moon Conference on Lifelong Learning Agents (CoLLAs), 2024 | 7* | 2024 |
| Observations on K-Image Expansion of Image-Mixing Augmentation J Jeong, S Cha, J Choi, S Yun, T Moon, Y Yoo IEEE Access, 2023 | 6* | 2023 |
| DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling S Joo, S Cha, T Moon AAAI Conference on Artificial Intelligence, 2019 | 6 | 2019 |
| Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning S Cha, K Cho, T Moon Forty-first International Conference on Machine Learning (ICML), 2024 | 3* | 2024 |
| 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 Winter Conference on Applications of Computer Vision (WACV), 2024 | 2* | 2024 |
| Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models S Cha, S Cho, D Hwang, M Lee arXiv preprint arXiv:2408.06621, 2024 | 1 | 2024 |
Taesup Moon (문태섭)Professor, Department of Electrical and Computer Engineering, Seoul National UniversityVerified email at snu.ac.kr
