Sungmin Cha

According to our database, Sungmin Cha authored at least 38 papers between 2017 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Cross-Modal Watermarking for Authentic Audio Recovery and Tamper Localization in Synthesized Audiovisual Forgeries.

Minyoung Kim, Sehwan Park, Sungmin Cha, Paul Hongsuck Seo

CoRR, July, 2025

Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection.

Geonu Lee, Yujeong Oh, Geonhui Jang, Soyoung Lee, Jeonghyo Song, Sungmin Cha, Young Joon Yoo

CoRR, June, 2025

Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation.

Sungmin Cha, Kyunghyun Cho

CoRR, May, 2025

Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols.

Yongwoo Kim, Sungmin Cha, Donghyun Kim

CoRR, March, 2025

Cost-Efficient Continual Learning with Sufficient Exemplar Memory.

Dongkyu Cho, Taesup Moon, Rumi Chunara, Kyunghyun Cho, Sungmin Cha

CoRR, February, 2025

Hyperparameters in Continual Learning: A Reality Check.

Sungmin Cha, Kyunghyun Cho

Trans. Mach. Learn. Res., 2025

Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs.

Sungmin Cha, Sungjun Cho, Dasol Hwang, Moontae Lee

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Models.

Hyesong Choi, Daeun Kim, Sungmin Cha, Kwang Moo Yi, Dongbo Min

CoRR, 2024

Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models.

Sungmin Cha, Sungjun Cho, Dasol Hwang, Moontae Lee

CoRR, 2024

NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations.

Sungmin Cha, Naeun Ko, Heewoong Choi, Youngjoon Yoo, Taesup Moon

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning.

Sungmin Cha, Kyunghyun Cho, Taesup Moon

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training.

Hyesong Choi, Hyejin Park, Kwang Moo Yi, Sungmin Cha, Dongbo Min

Proceedings of the Computer Vision - ECCV 2024, 2024

Towards realistic incremental scenario in class incremental semantic segmentation.

Jihwan Kwak, Sungmin Cha, Taesup Moon

Proceedings of the Conference on Lifelong Learning Agents, 2024

Towards More Diverse Evaluation of Class Incremental Learning: Representation Learning Perspective.

Sungmin Cha, Jihwan Kwak, Dongsub Shim, Hyunwoo Kim, Moontae Lee, Honglak Lee, Taesup Moon

Proceedings of the Conference on Lifelong Learning Agents, 2024

Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers.

Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, Moontae Lee

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised Representation Learning.

Sungmin Cha, Taesup Moon

CoRR, 2023

Observations on K-Image Expansion of Image-Mixing Augmentation.

Joonhyun Jeong, Sungmin Cha, Jongwon Choi, Sangdoo Yun, Taesup Moon, Youngjoon Yoo

IEEE Access, 2023

Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning.

Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong, Moontae Lee, Taesup Moon

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Knowledge Unlearning for Mitigating Privacy Risks in Language Models.

Joel Jang, Dongkeun Yoon, Sohee Yang, Sungmin Cha, Moontae Lee, Lajanugen Logeswaran, Minjoon Seo

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

Is Continual Learning Truly Learning Representations Continually?

Sungmin Cha, Dongsub Shim, Hyunwoo Kim, Moontae Lee, Honglak Lee, Taesup Moon

CoRR, 2022

Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning.

Sungmin Cha, Soonwon Hong, Moontae Lee, Taesup Moon

CoRR, 2022

2021

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising.

Sungmin Cha, Seonwoo Min, Sungroh Yoon, Taesup Moon

CoRR, 2021

Observations on K-image Expansion of Image-Mixing Augmentation for Classification.

Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi

CoRR, 2021

Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense against Gray- and Black-Box Attack.

Sungmin Cha, Naeun Ko, Youngjoon Yoo, Taesup Moon

CoRR, 2021

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning.

Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, Taesup Moon

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images.

Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon

Proceedings of the 9th International Conference on Learning Representations, 2021

CPR: Classifier-Projection Regularization for Continual Learning.

Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flávio P. Calmon, Taesup Moon

Proceedings of the 9th International Conference on Learning Representations, 2021

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise.

Jaeseok Byun, Sungmin Cha, Taesup Moon

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

CPR: Classifier-Projection Regularization for Continual Learning.

Sungmin Cha, Hsiang Hsu, Flávio P. Calmon, Taesup Moon

CoRR, 2020

Adaptive Group Sparse Regularization for Continual Learning.

Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

CoRR, 2020

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization.

Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

GAN2GAN: Generative Noise Learning for Blind Image Denoising with Single Noisy Images.

Sungmin Cha, Taeeon Park, Taesup Moon

CoRR, 2019

Uncertainty-based Continual Learning with Adaptive Regularization.

Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Fully Convolutional Pixel Adaptive Image Denoiser.

Sungmin Cha, Taesup Moon

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling.

Sunghwan Joo, Sungmin Cha, Taesup Moon

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

UDLR Convolutional Network for Adaptive Image Denoiser.

Sungmin Cha, Taesup Moon

Proceedings of the Robot Intelligence Technology and Applications, 2018

Neural Adaptive Image Denoiser.

Sungmin Cha, Taesup Moon

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

Neural Affine Grayscale Image Denoising.

Sungmin Cha, Taesup Moon

CoRR, 2017