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<titleInfo><title>Clustering meets implicit generative models</title></titleInfo>


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<name type="personal">
  <namePart type="given">Francesco</namePart>
  <namePart type="family">Locatello</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">26cfd52f-2483-11ee-8040-88983bcc06d4</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-4850-0683</description></name>
<name type="personal">
  <namePart type="given">Damien</namePart>
  <namePart type="family">Vincent</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Ilya</namePart>
  <namePart type="family">Tolstikhin</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Gunnar</namePart>
  <namePart type="family">Ratsch</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Sylvain</namePart>
  <namePart type="family">Gelly</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Bernhard</namePart>
  <namePart type="family">Scholkopf</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>







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  <namePart>International Conference on Machine Learning</namePart>
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<abstract lang="eng">Clustering is a cornerstone of unsupervised learning which can be thought as disentangling multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we particularize for variational autoencoders. Relying on an additional set of discriminators, we propose a competitive procedure in which the models only need to approximate the portion of the data distribution from which they can produce realistic samples. As a byproduct, each model is simpler to train, and a clustering interpretation arises naturally from the partitioning of the training points among the models. We empirically show that our approach splits the training distribution in a reasonable way and increases the quality of the generated samples.</abstract>

<originInfo><dateIssued encoding="w3cdtf">2018</dateIssued><place><placeTerm type="text">Vancouver, Canada</placeTerm></place>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>6th International Conference on Learning Representations</title></titleInfo>
  <identifier type="arXiv">1804.11130</identifier>
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</part>
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<bibliographicCitation>
<apa>Locatello, F., Vincent, D., Tolstikhin, I., Ratsch, G., Gelly, S., &amp;#38; Scholkopf, B. (2018). Clustering meets implicit generative models. In &lt;i&gt;6th International Conference on Learning Representations&lt;/i&gt;. Vancouver, Canada.</apa>
<chicago>Locatello, Francesco, Damien Vincent, Ilya Tolstikhin, Gunnar Ratsch, Sylvain Gelly, and Bernhard Scholkopf. “Clustering Meets Implicit Generative Models.” In &lt;i&gt;6th International Conference on Learning Representations&lt;/i&gt;, 2018.</chicago>
<mla>Locatello, Francesco, et al. “Clustering Meets Implicit Generative Models.” &lt;i&gt;6th International Conference on Learning Representations&lt;/i&gt;, 2018.</mla>
<ieee>F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, and B. Scholkopf, “Clustering meets implicit generative models,” in &lt;i&gt;6th International Conference on Learning Representations&lt;/i&gt;, Vancouver, Canada, 2018.</ieee>
<short>F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, B. Scholkopf, in:, 6th International Conference on Learning Representations, 2018.</short>
<ama>Locatello F, Vincent D, Tolstikhin I, Ratsch G, Gelly S, Scholkopf B. Clustering meets implicit generative models. In: &lt;i&gt;6th International Conference on Learning Representations&lt;/i&gt;. ; 2018.</ama>
<ista>Locatello F, Vincent D, Tolstikhin I, Ratsch G, Gelly S, Scholkopf B. 2018. Clustering meets implicit generative models. 6th International Conference on Learning Representations. International Conference on Machine Learning.</ista>
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