title: Training Data Protection with Compositional Diffusion Models
summary: We introduce Compartmentalized Diffusion Models (CDM), a method to train
  different diffusion models (or prompts) on distinct data sources and
  arbitrarily compose them at inference time. The individual models can be
  trained in isolation, at different times, and on different distributions and
  domains and can be later composed to achieve performance comparable to a
  paragon model trained on all data simultaneously. Furthermore, each model only
  contains information about the subset of the data it was exposed to during
  training, enabling several forms of training data protection. In particular,
  CDMs are the first method to enable both selective forgetting and continual
  learning for large-scale diffusion models, as well as allowing serving
  customized models based on the user's access rights. CDMs also allow
  determining the importance of a subset of the data in generating particular
  samples.
paper_authors._id: 64d06afe1720c7a483026706
paper_authors.name: Aditya Golatkar
paper_authors._id: 64d06afe1720c7a483026707
paper_authors.name: Alessandro Achille
paper_authors._id: 64d06afe1720c7a483026708
paper_authors.name: Ashwin Swaminathan
paper_authors._id: 64d06afe1720c7a483026709
paper_authors.name: Stefano Soatto
paper_title: Training Data Protection with Compositional Diffusion Models
paper_summary: We introduce Compartmentalized Diffusion Models (CDM), a method to train
  different diffusion models (or prompts) on distinct data sources and
  arbitrarily compose them at inference time. The individual models can be
  trained in isolation, at different times, and on different distributions and
  domains and can be later composed to achieve performance comparable to a
  paragon model trained on all data simultaneously. Furthermore, each model only
  contains information about the subset of the data it was exposed to during
  training, enabling several forms of training data protection. In particular,
  CDMs are the first method to enable both selective forgetting and continual
  learning for large-scale diffusion models, as well as allowing serving
  customized models based on the user's access rights. CDMs also allow
  determining the importance of a subset of the data in generating particular
  samples.
paper_ai_keywords: Compartmentalized Diffusion Models (CDM) diffusion models prompts data sources inference time training data protection selective forgetting continual learning large-scale diffusion models user's access rights determining the importance of a subset of the data