4 papers
Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning
Hyungjun Joo, Sehwan Kim, Hyeonggeun Han +2
Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual training examples. Effective mit…
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…