Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for
  Text-to-Image Diffusion Model Unlearning
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.
67073b95fb83bdd95a196060
Saemi Moon
admin_assigned
2024-10-10T10:00:31.147Z
644896af4988ee01f2a917d4
/avatars/0e40133c2957541071e2ca41b2d8a13d.svg
saemi moon
user
hi-sammy
67073b95fb83bdd95a196061
Minjong Lee
claimed_verified
2024-10-10T08:01:20.890Z
650a4546a0f81fbc0a94ef1a
/avatars/4aed2b283e0c0e2567c14eb81adf809a.svg
Minjong Lee
user
Minjong
67073b95fb83bdd95a196062
Sangdon Park
67073b95fb83bdd95a196063
Dongwoo Kim
Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for
  Text-to-Image Diffusion Model Unlearning
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.
A comprehensive evaluation framework is introduced to analyze the side effects and limitations of model unlearning in text-to-image diffusion models across various scenarios.
text-to-image diffusion models model unlearning image quality