title: How Many Van Goghs Does It Take to Van Gogh? Finding the Imitation
    Threshold
summary: Text-to-image models are trained using large datasets collected by scraping
  image-text pairs from the internet. These datasets often include private,
  copyrighted, and licensed material. Training models on such datasets enables
  them to generate images with such content, which might violate copyright laws
  and individual privacy. This phenomenon is termed imitation -- generation of
  images with content that has recognizable similarity to its training images. In
  this work we study the relationship between a concept's frequency in the
  training dataset and the ability of a model to imitate it. We seek to determine
  the point at which a model was trained on enough instances to imitate a concept
  -- the imitation threshold. We posit this question as a new problem: Finding
  the Imitation Threshold (FIT) and propose an efficient approach that estimates
  the imitation threshold without incurring the colossal cost of training
  multiple models from scratch. We experiment with two domains -- human faces and
  art styles -- for which we create four datasets, and evaluate three
  text-to-image models which were trained on two pretraining datasets. Our
  results reveal that the imitation threshold of these models is in the range of
  200-600 images, depending on the domain and the model. The imitation threshold
  can provide an empirical basis for copyright violation claims and acts as a
  guiding principle for text-to-image model developers that aim to comply with
  copyright and privacy laws. We release the code and data at
  https://github.com/vsahil/MIMETIC-2.git and the project's website is
  hosted at https://how-many-van-goghs-does-it-take.github.io.
paper_authors._id: 6717dbde394c8723dfe98018
paper_authors.name: Sahil Verma
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-23T07:33:22.758Z
paper_authors._id: 63ba371eefe99543b35701ab
paper_authors.avatarUrl: /avatars/87e700af5b31caa4a2680701a897fbd9.svg
paper_authors.fullname: Sahil Verma
paper_authors.type: user
paper_authors.user: vsahil
paper_authors._id: 6717dbde394c8723dfe98019
paper_authors.name: Royi Rassin
paper_authors._id: 6717dbde394c8723dfe9801a
paper_authors.name: Arnav Das
paper_authors._id: 6717dbde394c8723dfe9801b
paper_authors.name: Gantavya Bhatt
paper_authors._id: 6717dbde394c8723dfe9801c
paper_authors.name: Preethi Seshadri
paper_authors._id: 6717dbde394c8723dfe9801d
paper_authors.name: Chirag Shah
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T08:46:07.107Z
paper_authors._id: 6624485b0e31d65ecce09748
paper_authors.avatarUrl: /avatars/b78085aa72e673e570843b85b116f8bc.svg
paper_authors.fullname: Chirag Shah
paper_authors.type: user
paper_authors.user: chiragshah
paper_authors._id: 6717dbde394c8723dfe9801e
paper_authors.name: Jeff Bilmes
paper_authors._id: 6717dbde394c8723dfe9801f
paper_authors.name: Hannaneh Hajishirzi
paper_authors._id: 6717dbde394c8723dfe98020
paper_authors.name: Yanai Elazar
paper_title: How Many Van Goghs Does It Take to Van Gogh? Finding the Imitation
    Threshold
paper_summary: Text-to-image models are trained using large datasets collected by scraping
  image-text pairs from the internet. These datasets often include private,
  copyrighted, and licensed material. Training models on such datasets enables
  them to generate images with such content, which might violate copyright laws
  and individual privacy. This phenomenon is termed imitation -- generation of
  images with content that has recognizable similarity to its training images. In
  this work we study the relationship between a concept's frequency in the
  training dataset and the ability of a model to imitate it. We seek to determine
  the point at which a model was trained on enough instances to imitate a concept
  -- the imitation threshold. We posit this question as a new problem: Finding
  the Imitation Threshold (FIT) and propose an efficient approach that estimates
  the imitation threshold without incurring the colossal cost of training
  multiple models from scratch. We experiment with two domains -- human faces and
  art styles -- for which we create four datasets, and evaluate three
  text-to-image models which were trained on two pretraining datasets. Our
  results reveal that the imitation threshold of these models is in the range of
  200-600 images, depending on the domain and the model. The imitation threshold
  can provide an empirical basis for copyright violation claims and acts as a
  guiding principle for text-to-image model developers that aim to comply with
  copyright and privacy laws. We release the code and data at
  https://github.com/vsahil/MIMETIC-2.git and the project's website is
  hosted at https://how-many-van-goghs-does-it-take.github.io.
paper_ai_keywords: text-to-image models image-text pairs imitation imitation threshold Finding the Imitation Threshold (FIT)