Neural Affine Grayscale Image Denoising. - Sungmin Cha; Taesup Moon (CoRR, 2017)

Neural Adaptive Image Denoiser. - Sungmin Cha; Taesup Moon (ICASSP, 2018)

UDLR Convolutional Network for Adaptive Image Denoiser. - Sungmin Cha; Taesup Moon (RiTA, 2018)

DoPAMINE: Double-Sided Masked CNN for Pixel Adaptive Multiplicative Noise Despeckling. - Sunghwan Joo; Sungmin Cha; Taesup Moon (AAAI, 2019)

Fully Convolutional Pixel Adaptive Image Denoiser. - Sungmin Cha; Taesup Moon (ICCV, 2019)

Uncertainty-based Continual Learning with Adaptive Regularization. - Hongjoon Ahn; Sungmin Cha; Donggyu Lee; Taesup Moon (NeurIPS, 2019)

GAN2GAN: Generative Noise Learning for Blind Image Denoising with Single Noisy Images. - Sungmin Cha; Taeeon Park; Taesup Moon (CoRR, 2019)

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. - Sangwon Jung; Hongjoon Ahn; Sungmin Cha; Taesup Moon (NeurIPS, 2020)

Adaptive Group Sparse Regularization for Continual Learning. - Sangwon Jung; Hongjoon Ahn; Sungmin Cha; Taesup Moon (CoRR, 2020)

CPR: Classifier-Projection Regularization for Continual Learning. - Sungmin Cha; Hsiang Hsu; Flávio P. Calmon; Taesup Moon (CoRR, 2020)

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. - Jaeseok Byun; Sungmin Cha; Taesup Moon (CVPR, 2021)

CPR: Classifier-Projection Regularization for Continual Learning. - Sungmin Cha; Hsiang Hsu; Taebaek Hwang; Flávio P. Calmon; Taesup Moon (ICLR, 2021)

GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images. - Sungmin Cha; Taeeon Park; Byeongjoon Kim; Jongduk Baek; Taesup Moon (ICLR, 2021)

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. - Sungmin Cha; Beomyoung Kim; Youngjoon Yoo; Taesup Moon (NeurIPS, 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)

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)

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising. - Sungmin Cha; Seonwoo Min; Sungroh Yoon; Taesup Moon (CoRR, 2021)

Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning. - Sungmin Cha; Soonwon Hong; Moontae Lee; Taesup Moon (CoRR, 2022)

Is Continual Learning Truly Learning Representations Continually? - Sungmin Cha; Dongsub Shim; Hyunwoo Kim; Moontae Lee; Honglak Lee; Taesup Moon (CoRR, 2022)

Knowledge Unlearning for Mitigating Privacy Risks in Language Models. - Joel Jang; Dongkeun Yoon; Sohee Yang; Sungmin Cha; Moontae Lee; Lajanugen Logeswaran; Minjoon Seo (ACL (1), 2023)

Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning. - Sungmin Cha; Sungjun Cho; Dasol Hwang; Sunwon Hong; Moontae Lee; Taesup Moon (CVPR, 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)

Sy-CON: Symmetric Contrastive Loss for Continual Self-Supervised Representation Learning. - Sungmin Cha; Taesup Moon (CoRR, 2023)

Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers. - Sungmin Cha; Sungjun Cho; Dasol Hwang; Honglak Lee; Taesup Moon; Moontae Lee (AAAI, 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 (CoLLAs, 2024)

Towards realistic incremental scenario in class incremental semantic segmentation. - Jihwan Kwak; Sungmin Cha; Taesup Moon (CoLLAs, 2024)

Salience-Based Adaptive Masking: Revisiting Token Dynamics for Enhanced Pre-training. - Hyesong Choi; Hyejin Park 003; Kwang Moo Yi; Sungmin Cha; Dongbo Min (ECCV (78), 2024)

Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning. - Sungmin Cha; Kyunghyun Cho; Taesup Moon (ICML, 2024)

NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations. - Sungmin Cha; Naeun Ko; Heewoong Choi; Youngjoon Yoo; Taesup Moon (WACV, 2024)

Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models. - Sungmin Cha; Sungjun Cho; Dasol Hwang; Moontae Lee (CoRR, 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 Parameter-Efficient Knowledge Unlearning for LLMs. - Sungmin Cha; Sungjun Cho; Dasol Hwang; Moontae Lee (ICLR, 2025)

Hyperparameters in Continual Learning: A Reality Check. - Sungmin Cha; Kyunghyun Cho (Trans. Mach. Learn. Res., 2025)

Cost-Efficient Continual Learning with Sufficient Exemplar Memory. - Dongkyu Cho; Taesup Moon; Rumi Chunara; Kyunghyun Cho; Sungmin Cha (CoRR, 2025)

Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols. - Yongwoo Kim; Sungmin Cha; Donghyun Kim (CoRR, 2025)

Why Knowledge Distillation Works in Generative Models: A Minimal Working Explanation. - Sungmin Cha; Kyunghyun Cho (CoRR, 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, 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, 2025)