title: Unsupervised Compositional Concepts Discovery with Text-to-Image
    Generative Models
summary: Text-to-image generative models have enabled high-resolution image synthesis
  across different domains, but require users to specify the content they wish to
  generate. In this paper, we consider the inverse problem -- given a collection
  of different images, can we discover the generative concepts that represent
  each image? We present an unsupervised approach to discover generative concepts
  from a collection of images, disentangling different art styles in paintings,
  objects, and lighting from kitchen scenes, and discovering image classes given
  ImageNet images. We show how such generative concepts can accurately represent
  the content of images, be recombined and composed to generate new artistic and
  hybrid images, and be further used as a representation for downstream
  classification tasks.
paper_authors._id: 6482be9b474d4d463d4e941c
paper_authors.name: Nan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-09T16:55:34.264Z
paper_authors._id: 62db5ae193fc0e52427a803a
paper_authors.avatarUrl: /avatars/251e209b60178a2ffc555b2ecdd29cf1.svg
paper_authors.fullname: Nan Liu
paper_authors.type: user
paper_authors.user: nanliu
paper_authors._id: 6482be9b474d4d463d4e941d
paper_authors.name: Yilun Du
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T13:40:30.390Z
paper_authors._id: 63c9bd445fdc575773c732fe
paper_authors.avatarUrl: /avatars/def472d1ab3fbf751225357c0932ae7e.svg
paper_authors.fullname: Yilun Du
paper_authors.type: user
paper_authors.user: yilundu
paper_authors._id: 6482be9b474d4d463d4e941e
paper_authors.name: Shuang Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-09T16:55:29.788Z
paper_authors._id: 629e4ad01fec321a2d2abfa3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/629e4ad01fec321a2d2abfa3/LwMzgQr9RH5arKOBNR043.jpeg
paper_authors.fullname: Shuang Li
paper_authors.type: user
paper_authors.user: Shuang59
paper_authors._id: 6482be9b474d4d463d4e941f
paper_authors.name: Joshua B. Tenenbaum
paper_authors._id: 6482be9b474d4d463d4e9420
paper_authors.name: Antonio Torralba
paper_title: Unsupervised Compositional Concepts Discovery with Text-to-Image
    Generative Models
paper_summary: Text-to-image generative models have enabled high-resolution image synthesis
  across different domains, but require users to specify the content they wish to
  generate. In this paper, we consider the inverse problem -- given a collection
  of different images, can we discover the generative concepts that represent
  each image? We present an unsupervised approach to discover generative concepts
  from a collection of images, disentangling different art styles in paintings,
  objects, and lighting from kitchen scenes, and discovering image classes given
  ImageNet images. We show how such generative concepts can accurately represent
  the content of images, be recombined and composed to generate new artistic and
  hybrid images, and be further used as a representation for downstream
  classification tasks.
paper_ai_keywords: text-to-image generative models high-resolution image synthesis unsupervised approach generative concepts disentangling art styles object lighting kitchen scenes ImageNet images recombined composed new artistic images hybrid images representation downstream classification tasks