title: Machine Unlearning for Image-to-Image Generative Models
summary: Machine unlearning has emerged as a new paradigm to deliberately forget data
  samples from a given model in order to adhere to stringent regulations.
  However, existing machine unlearning methods have been primarily focused on
  classification models, leaving the landscape of unlearning for generative
  models relatively unexplored. This paper serves as a bridge, addressing the gap
  by providing a unifying framework of machine unlearning for image-to-image
  generative models. Within this framework, we propose a
  computationally-efficient algorithm, underpinned by rigorous theoretical
  analysis, that demonstrates negligible performance degradation on the retain
  samples, while effectively removing the information from the forget samples.
  Empirical studies on two large-scale datasets, ImageNet-1K and Places-365,
  further show that our algorithm does not rely on the availability of the retain
  samples, which further complies with data retention policy. To our best
  knowledge, this work is the first that represents systemic, theoretical,
  empirical explorations of machine unlearning specifically tailored for
  image-to-image generative models. Our code is available at
  https://github.com/jpmorganchase/l2l-generator-unlearning.
paper_authors._id: 65bc76aa4d1795fa8e1a0a6c
paper_authors.name: Guihong Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-02T15:57:56.892Z
paper_authors._id: 63372059267cee4d068d123a
paper_authors.avatarUrl: /avatars/ef3720ff6bfcf3bd72c19d89f738221a.svg
paper_authors.fullname: Li
paper_authors.type: user
paper_authors.user: ghl
paper_authors._id: 65bc76aa4d1795fa8e1a0a6d
paper_authors.name: Hsiang Hsu
paper_authors._id: 65bc76aa4d1795fa8e1a0a6e
paper_authors.name: Chun-Fu
paper_authors._id: 65bc76aa4d1795fa8e1a0a6f
paper_authors.name: Chen
paper_authors._id: 65bc76aa4d1795fa8e1a0a70
paper_authors.name: Radu Marculescu
paper_title: Machine Unlearning for Image-to-Image Generative Models
paper_summary: Machine unlearning has emerged as a new paradigm to deliberately forget data
  samples from a given model in order to adhere to stringent regulations.
  However, existing machine unlearning methods have been primarily focused on
  classification models, leaving the landscape of unlearning for generative
  models relatively unexplored. This paper serves as a bridge, addressing the gap
  by providing a unifying framework of machine unlearning for image-to-image
  generative models. Within this framework, we propose a
  computationally-efficient algorithm, underpinned by rigorous theoretical
  analysis, that demonstrates negligible performance degradation on the retain
  samples, while effectively removing the information from the forget samples.
  Empirical studies on two large-scale datasets, ImageNet-1K and Places-365,
  further show that our algorithm does not rely on the availability of the retain
  samples, which further complies with data retention policy. To our best
  knowledge, this work is the first that represents systemic, theoretical,
  empirical explorations of machine unlearning specifically tailored for
  image-to-image generative models. Our code is available at
  https://github.com/jpmorganchase/l2l-generator-unlearning.
paper_ai_summary: A novel framework and algorithm for machine unlearning in image-to-image generative models is introduced, showing minimal performance loss and compliance with data retention policies.
paper_ai_keywords: machine unlearning image-to-image generative models performance degradation retain samples forget samples Empirical studies ImageNet-1K Places-365 data retention policy