4 papers
From Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models
Hyeonggeun Han, Taehyun Cho, Sangwoo Hong
Large vision-language models often exhibit object hallucination, generating claims that are not supported by the input image. To mitigate this issue, we propose Evidence-Guided Vis…
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…