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We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densities and approximate Wasserstein distances. We show that an independent critic trained to approximate Wasserstein distance between the validation set and the generator distribution helps detect overfitting. Finally, we use ideas from the one-shot learning literature to develop a novel fast learning critic.

Pertenece a

UCL University College London Eprints  

Autor(es)

Danihelka, I -  Lakshminarayanan, B -  Uria, B -  Wierstra, D -  Dayan, P - 

Id.: 69877543

Versión: 1.0

Estado: Final

Palabras clavecs.LG, cs.LG - 

Tipo de recurso: Article  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] null

Fecha de contribución: 29-may-2017

Contacto:

Localización:

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