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Adversarial Training Improves Joint Energy-Based Generative Modelling

Adversarial Training Improves Joint Energy-Based Generative Modelling

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2691903765

Adversarial Training Improves Joint Energy-Based Generative Modelling

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Full title

Adversarial Training Improves Joint Energy-Based Generative Modelling

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2022-07

Language

English

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Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

Subjects

Subjects and topics

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Scope and Contents

Contents

We propose the novel framework for generative modelling using hybrid energy-based models. In our method we combine the interpretable input gradients of the robust classifier and Langevin Dynamics for sampling. Using the adversarial training we improve not only the training stability, but robustness and generative modelling of the joint energy-based...

Alternative Titles

Full title

Adversarial Training Improves Joint Energy-Based Generative Modelling

Authors, Artists and Contributors

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Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2691903765

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2691903765

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E-ISSN

2331-8422

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