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Sungmin Cha (차성민)
Sungmin Cha (차성민)
Faculty Fellow, New York University
nyu.edu의 이메일 확인됨 - 홈페이지
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Uncertainty-based continual learning with adaptive regularization
H Ahn, S Cha, D Lee, T Moon
Advances in Neural Information Processing Systems (NeurIPS) 2019, 2019
1902019
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
1182020
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
1032020
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
652021
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
602021
Fully convolutional pixel adaptive image denoiser
S Cha, T Moon
International Conference on Computer Vision (ICCV) 2019, 4160-4169, 2019
542019
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
522022
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
482021
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
45*2021
Neural adaptive image denoiser
S Cha, T Moon
International Conference on Acoustics, Speech and Signal Processing (ICASSP …, 2018
262018
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
12*2023
Learning to unlearn: Instance-wise unlearning for pre-trained classifiers
S Cha, S Cho, D Hwang, H Lee, T Moon, M Lee
Proceedings of the AAAI Conference on Artificial Intelligence 38 (10), 11186 …, 2024
82024
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
42019
Udlr convolutional network for adaptive image denoiser
S Cha, T Moon
Robot Intelligence Technology and Applications: 6th International Conference …, 2019
32019
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
12024
Augmenting Negative Representation for Continual Self-Supervised Learning
S Cha, K Cho, T Moon
1*2023
Towards Realistic Incremental Scenario in Class Incremental Semantic Segmentation
J Kwak, S Cha, T Moon
arXiv preprint arXiv:2405.09858, 2024
2024
Hyperparameters in Continual Learning: a Reality Check
S Cha, K Cho
arXiv preprint arXiv:2403.09066, 2024
2024
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