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Sungmin Cha (차성민)
Sungmin Cha (차성민)
Faculty Fellow, New York University
Bestätigte E-Mail-Adresse bei nyu.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Uncertainty-based continual learning with adaptive regularization
H Ahn, S Cha, D Lee, T Moon
Advances in Neural Information Processing Systems (NeurIPS), 2019
2662019
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
1832023
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
1482020
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
1422020
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
992021
CPR: Classifier-Projection Regularization for Continual Learning
S Cha, H Hsu, T Hwang, FP Calmon, T Moon
International Conference on Learning Representations (ICLR), 2021
892021
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
622021
Fully convolutional pixel adaptive image denoiser
S Cha, T Moon
International Conference on Computer Vision (ICCV), 2019
622019
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
55*2021
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
302024
Neural adaptive image denoiser
S Cha, T Moon
International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2018
282018
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
25*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
92024
Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning
S Cha, K Cho, T Moon
Forty-first International Conference on Machine Learning (ICML), 2024
8*2024
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling
S Joo, S Cha, T Moon
AAAI Conference on Artificial Intelligence, 2019
72019
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
52024
Observations on K-Image Expansion of Image-Mixing Augmentation
J Jeong, S Cha, J Choi, S Yun, T Moon, Y Yoo
IEEE Access, 2023
5*2023
Towards more diverse evaluation of class incremental learning: Representation learning perspective
S Cha, J Kwak, D Shim, H Kim, M Lee, H Lee, T Moon
Conference on Lifelong Learning Agents (CoLLAs), 2025
4*2025
Hyperparameters in Continual Learning: A Reality Check
S Cha, K Cho
arXiv preprint arXiv:2403.09066, 2024
32024
Udlr convolutional network for adaptive image denoiser
S Cha, T Moon
Robot Intelligence Technology and Applications: 6th International Conference …, 2019
32019
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