5 papers
Continual Data Unlearning in Diffusion Models via Transition-based Regularization
Sunbeom Jeong, Sehwan Kim, Sangwoo Hong +1
Data unlearning in diffusion models aims to remove the influence of specific training examples without suppressing the broader concepts they represent. However, when deletion reque…
Adjusting Initial Noise to Mitigate Memorization in Text-to-Image Diffusion Models
Hyeonggeun Han, Sehwan Kim, Hyungjun Joo +2
Despite their impressive generative capabilities, text-to-image diffusion models often memorize and replicate training data, prompting serious concerns over privacy and copyright.…
Constructing Fair Latent Space for Intersection of Fairness and Explainability
Hyungjun Joo, Hyeonggeun Han, Sehwan Kim +2
As the use of machine learning models has increased, numerous studies have aimed to enhance fairness. However, research on the intersection of fairness and explainability remains i…
Mitigating Spurious Correlations via Disagreement Probability
Hyeonggeun Han, Sehwan Kim, Hyungjun Joo +2
Models trained with empirical risk minimization (ERM) are prone to be biased towards spurious correlations between target labels and bias attributes, which leads to poor performanc…
Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition
Youngseok Yoon, Sangwoo Hong, Hyungjun Joo +3
Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the prob…