---
res:
  bibo_abstract:
  - "The key idea behind the unsupervised learning of disentangled representations\r\nis
    that real-world data is generated by a few explanatory factors of variation\r\nwhich
    can be recovered by unsupervised learning algorithms. In this paper, we\r\nprovide
    a sober look at recent progress in the field and challenge some common\r\nassumptions.
    We first theoretically show that the unsupervised learning of\r\ndisentangled
    representations is fundamentally impossible without inductive\r\nbiases on both
    the models and the data. Then, we train more than 12000 models\r\ncovering most
    prominent methods and evaluation metrics in a reproducible\r\nlarge-scale experimental
    study on seven different data sets. We observe that\r\nwhile the different methods
    successfully enforce properties ``encouraged'' by\r\nthe corresponding losses,
    well-disentangled models seemingly cannot be\r\nidentified without supervision.
    Furthermore, increased disentanglement does not\r\nseem to lead to a decreased
    sample complexity of learning for downstream tasks.\r\nOur results suggest that
    future work on disentanglement learning should be\r\nexplicit about the role of
    inductive biases and (implicit) supervision,\r\ninvestigate concrete benefits
    of enforcing disentanglement of the learned\r\nrepresentations, and consider a
    reproducible experimental setup covering\r\nseveral data sets.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Francesco
      foaf_name: Locatello, Francesco
      foaf_surname: Locatello
      foaf_workInfoHomepage: http://www.librecat.org/personId=26cfd52f-2483-11ee-8040-88983bcc06d4
    orcid: 0000-0002-4850-0683
  - foaf_Person:
      foaf_givenName: Stefan
      foaf_name: Bauer, Stefan
      foaf_surname: Bauer
  - foaf_Person:
      foaf_givenName: Mario
      foaf_name: Lucic, Mario
      foaf_surname: Lucic
  - foaf_Person:
      foaf_givenName: Gunnar
      foaf_name: Rätsch, Gunnar
      foaf_surname: Rätsch
  - foaf_Person:
      foaf_givenName: Sylvain
      foaf_name: Gelly, Sylvain
      foaf_surname: Gelly
  - foaf_Person:
      foaf_givenName: Bernhard
      foaf_name: Schölkopf, Bernhard
      foaf_surname: Schölkopf
  - foaf_Person:
      foaf_givenName: Olivier
      foaf_name: Bachem, Olivier
      foaf_surname: Bachem
  bibo_volume: 97
  dct_date: 2019^xs_gYear
  dct_language: eng
  dct_publisher: ML Research Press@
  dct_title: Challenging common assumptions in the unsupervised learning of disentangled
    representations@
...