Moon, Taesup

39 publications

ICCV 2025 An Efficient Post-Hoc Framework for Reducing Task Discrepancy of Text Encoders for Composed Image Retrieval Jaeseok Byun, Seokhyeon Jeong, Wonjae Kim, Sanghyuk Chun, Taesup Moon

ICLRW 2025 Cost-Efficient Continual Learning with Sufficient Exemplar Memory Dong Kyu Cho, Taesup Moon, Rumi Chunara, Kyunghyun Cho, Sungmin Cha

ICCV 2025 MA-CIR: A Multimodal Arithmetic Benchmark for Composed Image Retrieval Jaeseok Byun, Young Kyun Jang, Seokhyeon Jeong, Donghyun Kim, Taesup Moon

CVPR 2025 Multi-Group Proportional Representations for Text-to-Image Models Sangwon Jung, Alex Oesterling, Claudio Mayrink Verdun, Sajani Vithana, Taesup Moon, Flavio P. Calmon

NeurIPS 2025 Option-Aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning Hongjoon Ahn, Heewoong Choi, Jisu Han, Taesup Moon

ICLR 2025 Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline Hongjoon Ahn, Jinu Hyeon, Youngmin Oh, Bosun Hwang, Taesup Moon

WACV 2025 TLDR: Text Based Last-Layer Retraining for Debiasing Image Classifiers Juhyeon Park, Seokhyeon Jeong, Taesup Moon

ICLR 2024 Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline Donggyu Lee, Sangwon Jung, Taesup Moon

NeurIPS 2024 Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- a Theoretical and Empirical Study Sangwon Jung, Sumin Yu, Sanghyuk Chun, Taesup Moon

AAAI 2024 Learning to Unlearn: Instance-Wise Unlearning for Pre-Trained Classifiers Sungmin Cha, Sungjun Cho, Dasol Hwang, Honglak Lee, Taesup Moon, Moontae Lee

ICML 2024 Listwise Reward Estimation for Offline Preference-Based Reinforcement Learning Heewoong Choi, Sangwon Jung, Hongjoon Ahn, Taesup Moon

CVPR 2024 MAFA: Managing False Negatives for Vision-Language Pre-Training Jaeseok Byun, Dohoon Kim, Taesup Moon

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

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

CoLLAs 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

ICLR 2023 Re-Weighting Based Group Fairness Regularization via Classwise Robust Optimization Sangwon Jung, Taeeon Park, Sanghyuk Chun, Taesup Moon

CVPR 2023 Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning Sungmin Cha, Sungjun Cho, Dasol Hwang, Sunwon Hong, Moontae Lee, Taesup Moon

NeurIPS 2023 SwiFT: Swin 4D fMRI Transformer Peter Kim, Junbeom Kwon, Sunghwan Joo, Sangyoon Bae, Donggyu Lee, Yoonho Jung, Shinjae Yoo, Jiook Cha, Taesup Moon

AAAI 2023 Towards More Robust Interpretation via Local Gradient Alignment Sunghwan Joo, Seokhyeon Jeong, Juyeon Heo, Adrian Weller, Taesup Moon

NeurIPS 2022 Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P Wipf

ECCV 2022 GRIT-VLP: Grouped Mini-Batch Sampling for Efficient Vision and Language Pre-Training Jaeseok Byun, Taebaek Hwang, Jianlong Fu, Taesup Moon

CVPR 2022 Learning Fair Classifiers with Partially Annotated Group Labels Sangwon Jung, Sanghyuk Chun, Taesup Moon

ICLR 2021 CPR: Classifier-Projection Regularization for Continual Learning Sungmin Cha, Hsiang Hsu, Taebaek Hwang, Flavio Calmon, Taesup Moon

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

CVPR 2021 Fair Feature Distillation for Visual Recognition Sangwon Jung, Donggyu Lee, Taeeon Park, 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

ICCV 2021 SS-IL: Separated SoftMax for Incremental Learning Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, Taesup Moon

NeurIPS 2021 SSUL: Semantic Segmentation with Unknown Label for Exemplar-Based Class-Incremental Learning Sungmin Cha, Beomyoung Kim, YoungJoon Yoo, Taesup Moon

ICMLW 2021 Self-Supervised Iterative Contextual Smoothing for Efficient Adversarial Defense Against Gray- and Black-Box Attack Sungmin Cha, Naeun Ko, YoungJoon Yoo, Taesup Moon

NeurIPS 2020 Continual Learning with Node-Importance Based Adaptive Group Sparse Regularization Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon

NeurIPSW 2020 GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images Sungmin Cha, Taeeon Park, Byeongjoon Kim, Jongduk Baek, Taesup Moon

UAI 2020 Iterative Channel Estimation for Discrete Denoising Under Channel Uncertainty Hongjoon Ahn, Taesup Moon

AISTATS 2020 Unsupervised Neural Universal Denoiser for Finite-Input General-Output Noisy Channel Taeeon Park, Taesup Moon

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

NeurIPS 2019 Fooling Neural Network Interpretations via Adversarial Model Manipulation Juyeon Heo, Sunghwan Joo, Taesup Moon

AAAI 2019 Subtask Gated Networks for Non-Intrusive Load Monitoring Changho Shin, Sunghwan Joo, Jaeryun Yim, Hyoseop Lee, Taesup Moon, Wonjong Rhee

NeurIPS 2019 Uncertainty-Based Continual Learning with Adaptive Regularization Hongjoon Ahn, Sungmin Cha, Donggyu Lee, Taesup Moon

NeurIPS 2016 Neural Universal Discrete Denoiser Taesup Moon, Seonwoo Min, Byunghan Lee, Sungroh Yoon