Unsupervised Compositional Concepts Discovery with Text-to-Image
  Generative Models
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.
6482be9b474d4d463d4e941c
Nan Liu
claimed_verified
2023-06-09T16:55:34.264Z
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/avatars/251e209b60178a2ffc555b2ecdd29cf1.svg
Nan Liu
user
nanliu
6482be9b474d4d463d4e941d
Yilun Du
admin_assigned
2023-06-12T13:40:30.390Z
63c9bd445fdc575773c732fe
/avatars/def472d1ab3fbf751225357c0932ae7e.svg
Yilun Du
user
yilundu
6482be9b474d4d463d4e941e
Shuang Li
claimed_verified
2023-06-09T16:55:29.788Z
629e4ad01fec321a2d2abfa3
https://cdn-avatars.huggingface.co/v1/production/uploads/629e4ad01fec321a2d2abfa3/LwMzgQr9RH5arKOBNR043.jpeg
Shuang Li
user
Shuang59
6482be9b474d4d463d4e941f
Joshua B. Tenenbaum
6482be9b474d4d463d4e9420
Antonio Torralba
Unsupervised Compositional Concepts Discovery with Text-to-Image
  Generative Models
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.
An unsupervised method discovers generative concepts from images, enabling the creation of new images and use as a representation for classification tasks.
text-to-image generative models high-resolution image synthesis unsupervised approach generative concepts disentangling art styles objects lighting kitchen scenes image classes ImageNet image representation classification tasks