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ORCIDSungmin Cha
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2020 – today
- 2024
[c16]Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, Moontae Lee:
Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers. AAAI 2024: 11186-11194- [c15]Hyesong Choi, Hyejin Park, Kwang Moo Yi, Sungmin Cha, Dongbo Min:
Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training. ECCV (78) 2024: 343-359 - [c14]Sungmin Cha, Kyunghyun Cho, Taesup Moon:
Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning. ICML 2024 - [c13]Sungmin Cha, Naeun Ko, Heewoong Choi, Youngjoon Yoo, Taesup Moon:
NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations. WACV 2024: 3777-3787 - [i21]Sungmin Cha, Kyunghyun Cho:
Hyperparameters in Continual Learning: a Reality Check. CoRR abs/2403.09066 (2024) - [i20]Hyesong Choi, Hyejin Park, Kwang Moo Yi, Sungmin Cha, Dongbo Min:
Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training. CoRR abs/2404.08327 (2024) - [i19]Jihwan Kwak, Sungmin Cha, Taesup Moon:
Towards Realistic Incremental Scenario in Class Incremental Semantic Segmentation. CoRR abs/2405.09858 (2024) - [i18]Sungmin Cha, Sungjun Cho, Dasol Hwang, Moontae Lee:
Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models. CoRR abs/2408.06621 (2024) - 2023
- [j1]Joonhyun Jeong, Sungmin Cha, Jongwon Choi
, Sangdoo Yun, Taesup Moon, Youngjoon Yoo:
Observations on K-Image Expansion of Image-Mixing Augmentation. IEEE Access 11: 16631-16643 (2023) - [c12]Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, Minjoon Seo:
Knowledge Unlearning for Mitigating Privacy Risks in Language Models. ACL (1) 2023: 14389-14408 - [c11]Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong, Moontae Lee, Taesup Moon:
Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning. CVPR 2023: 20127-20136 - [i17]Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, Moontae Lee:
Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers. CoRR abs/2301.11578 (2023) - [i16]Sungmin Cha, Taesup Moon:
Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised Representation Learning. CoRR abs/2306.05101 (2023) - 2022
- [i15]Sungmin Cha, Soonwon Hong, Moontae Lee, Taesup Moon:
Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning. CoRR abs/2201.12559 (2022) - [i14]Sungmin Cha, Dongsub Shim, Hyunwoo Kim, Moontae Lee, Honglak Lee, Taesup Moon:
Is Continual Learning Truly Learning Representations Continually? CoRR abs/2206.08101 (2022) - [i13]Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, Minjoon Seo:
Knowledge Unlearning for Mitigating Privacy Risks in Language Models. CoRR abs/2210.01504 (2022) - 2021
- [c10]Jaeseok Byun, Sungmin Cha, Taesup Moon:
FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. CVPR 2021: 5768-5777 - [c9]Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flávio P. Calmon, Taesup Moon:
CPR: Classifier-Projection Regularization for Continual Learning. ICLR 2021 - [c8]Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon:
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images. ICLR 2021 - [c7]Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup Moon:
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. NeurIPS 2021: 10919-10930 - [i12]Jaeseok Byun, Sungmin Cha, Taesup Moon:
FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. CoRR abs/2105.10967 (2021) - [i11]Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup Moon:
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. CoRR abs/2106.11562 (2021) - [i10]Sungmin Cha, Naeun Ko, Youngjoon Yoo, Taesup Moon:
Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense against Gray- and Black-Box Attack. CoRR abs/2106.11644 (2021) - [i9]Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi:
Observations on K-image Expansion of Image-Mixing Augmentation for Classification. CoRR abs/2110.04248 (2021) - [i8]Sungmin Cha, Seonwoo Min, Sungroh Yoon, Taesup Moon:
Supervised Neural Discrete Universal Denoiser for Adaptive Denoising. CoRR abs/2111.12350 (2021) - 2020
- [c6]Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon:
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. NeurIPS 2020 - [i7]Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon:
Adaptive Group Sparse Regularization for Continual Learning. CoRR abs/2003.13726 (2020) - [i6]Sungmin Cha, Hsiang Hsu, Flávio P. Calmon, Taesup Moon:
CPR: Classifier-Projection Regularization for Continual Learning. CoRR abs/2006.07326 (2020)
2010 – 2019
- 2019
- [c5]Sunghwan Joo, Sungmin Cha, Taesup Moon:
DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling. AAAI 2019: 4031-4038 - [c4]Sungmin Cha, Taesup Moon:
Fully Convolutional Pixel Adaptive Image Denoiser. ICCV 2019: 4159-4168 - [c3]Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon:
Uncertainty-based Continual Learning with Adaptive Regularization. NeurIPS 2019: 4394-4404 - [i5]Sunghwan Joo, Sungmin Cha, Taesup Moon:
DoPAMINE: Double-sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling. CoRR abs/1902.02530 (2019) - [i4]Sungmin Cha, Taeeon Park, Taesup Moon:
GAN2GAN: Generative Noise Learning for Blind Image Denoising with Single Noisy Images. CoRR abs/1905.10488 (2019) - [i3]Hongjoon Ahn, Donggyu Lee, Sungmin Cha, Taesup Moon:
Uncertainty-based Continual Learning with Adaptive Regularization. CoRR abs/1905.11614 (2019) - 2018
- [c2]Sungmin Cha, Taesup Moon:
Neural Adaptive Image Denoiser. ICASSP 2018: 2981-2985 - [c1]Sungmin Cha, Taesup Moon:
UDLR Convolutional Network for Adaptive Image Denoiser. RiTA 2018: 55-61 - [i2]Sungmin Cha, Taesup Moon:
Fully Convolutional Pixel Adaptive Image Denoiser. CoRR abs/1807.07569 (2018) - 2017
- [i1]Sungmin Cha, Taesup Moon:
Neural Affine Grayscale Image Denoising. CoRR abs/1709.05672 (2017)
Coauthor Index
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