Adversarial Training Improves Joint Energy-Based Generative Modelling
Adversarial Training Improves Joint Energy-Based Generative Modelling
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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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...
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Adversarial Training Improves Joint Energy-Based Generative Modelling
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TN_cdi_proquest_journals_2691903765
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2691903765
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2331-8422