Wikipedia
Generative adversarial network

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5 Documents (Long List)

- [Seminar] Deep Latent Variable Models of Natural Language
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Both GANs and VAEs have been remarkably effective at modeling images, and the learned latent representations often correspond to interesting, semantically-meaningful representations of the observed data. In contrast, GANs and VAEs have been less successful at modeling natural language, but for different reasons. - GANs have difficulty dealing with discrete output spaces (such as natural language) as the resulting objective is no longer differentiable with respect to the generator. - VAEs can deal with discrete output spaces, but when a powerful model (e.g. LSTM) is used as a generator, the model learns to ignore the latent variable and simply becomes a language model.

2018-10-31 - The relativistic discriminator: a key element missing from standard GAN (2018) – Alexia Jolicoeur-Martineau
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> In this paper, I argue that standard GAN (SGAN) is missing a fundamental property, i.e., training the generator should not only increase the probability that fake data is real but also decrease the probability that real data is real

2018-07-05 - What a Disentangled Net We Weave: Representation Learning in VAEs (Pt. 1)
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2018-05-29 - An Adversarial Review of “Adversarial Generation of Natural Language”
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2018-01-01 - Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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2017-06-09

Properties

- sl:creationDate : 2017-06-09
- sl:creationTime : 2017-06-09T17:47:12Z
- sl:describedBy : https://en.wikipedia.org/wiki/Generative_adversarial_network
- rdf:type : sl:Tag
- skos:altLabel :
- GAN@en
- Generative adversarial networks@fr

- skos:prefLabel : Generative adversarial network