6 papers
Erasing Thousands of Concepts: Towards Scalable and Practical Concept Erasure for Text-to-Image Diffusion Models
Hoigi Seo, Byung Hyun Lee, Jaehyun Cho +2
Large-scale text-to-image (T2I) diffusion models deliver remarkable visual fidelity but pose safety risks due to their capacity to reproduce undesirable content, such as copyrighte…
Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models
Kyungryeol Lee, Kyeonghyun Lee, Seongmin Hong +2
Machine unlearning aims to remove specific outputs from trained models, often at the concept level, such as forgetting all occurrences of a particular celebrity or filtering conten…
Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation
Byung Hyun Lee, Sungjin Lim, Se Young Chun
Fine-tuning based concept erasing has demonstrated promising results in preventing generation of harmful contents from text-to-image diffusion models by removing target concepts wh…
Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysis
Byung Hyun Lee, Wongi Jeong, Woojae Han +2
Multiple instance learning (MIL) significantly reduced annotation costs via bag-level weak labels for large-scale images, such as histopathological whole slide images (WSIs). Howev…
Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate
Byung Hyun Lee, Sungjin Lim, Seunggyu Lee +2
Remarkable progress in text-to-image diffusion models has brought a major concern about potentially generating images on inappropriate or trademarked concepts. Concept erasing has…
Geometrical Properties of Text Token Embeddings for Strong Semantic Binding in Text-to-Image Generation
Hoigi Seo, Junseo Bang, Haechang Lee +3
Text-to-image (T2I) models often suffer from text-image misalignment in complex scenes involving multiple objects and attributes. Semantic binding has attempted to associate the ge…