title: Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for
    Text-to-Image Diffusion Model Unlearning
summary: As text-to-image diffusion models become advanced enough for commercial
  applications, there is also increasing concern about their potential for
  malicious and harmful use. Model unlearning has been proposed to mitigate the
  concerns by removing undesired and potentially harmful information from the
  pre-trained model. So far, the success of unlearning is mainly measured by
  whether the unlearned model can generate a target concept while maintaining
  image quality. However, unlearning is typically tested under limited scenarios,
  and the side effects of unlearning have barely been studied in the current
  literature. In this work, we thoroughly analyze unlearning under various
  scenarios with five key aspects. Our investigation reveals that every method
  has side effects or limitations, especially in more complex and realistic
  situations. By releasing our comprehensive evaluation framework with the source
  codes and artifacts, we hope to inspire further research in this area, leading
  to more reliable and effective unlearning methods.
paper_authors._id: 67073b95fb83bdd95a196060
paper_authors.name: Saemi Moon
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-10T10:00:31.147Z
paper_authors._id: 644896af4988ee01f2a917d4
paper_authors.avatarUrl: /avatars/0e40133c2957541071e2ca41b2d8a13d.svg
paper_authors.fullname: saemi moon
paper_authors.type: user
paper_authors.user: hi-sammy
paper_authors._id: 67073b95fb83bdd95a196061
paper_authors.name: Minjong Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-10T08:01:20.890Z
paper_authors._id: 650a4546a0f81fbc0a94ef1a
paper_authors.avatarUrl: /avatars/4aed2b283e0c0e2567c14eb81adf809a.svg
paper_authors.fullname: Minjong Lee
paper_authors.type: user
paper_authors.user: Minjong
paper_authors._id: 67073b95fb83bdd95a196062
paper_authors.name: Sangdon Park
paper_authors._id: 67073b95fb83bdd95a196063
paper_authors.name: Dongwoo Kim
paper_title: Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for
    Text-to-Image Diffusion Model Unlearning
paper_summary: As text-to-image diffusion models become advanced enough for commercial
  applications, there is also increasing concern about their potential for
  malicious and harmful use. Model unlearning has been proposed to mitigate the
  concerns by removing undesired and potentially harmful information from the
  pre-trained model. So far, the success of unlearning is mainly measured by
  whether the unlearned model can generate a target concept while maintaining
  image quality. However, unlearning is typically tested under limited scenarios,
  and the side effects of unlearning have barely been studied in the current
  literature. In this work, we thoroughly analyze unlearning under various
  scenarios with five key aspects. Our investigation reveals that every method
  has side effects or limitations, especially in more complex and realistic
  situations. By releasing our comprehensive evaluation framework with the source
  codes and artifacts, we hope to inspire further research in this area, leading
  to more reliable and effective unlearning methods.
paper_ai_keywords: text-to-image diffusion models model unlearning pre-trained model generation image quality side effects evaluation framework