A generative model is trained to match a data distribution by competing in a minimax game against a discriminator, reaching an equilibrium where the generator recovers the true distribution and the discriminator outputs 1/2 everywhere.
Pylearn2: a machine learning research library
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abstract
Pylearn2 is a machine learning research library. This does not just mean that it is a collection of machine learning algorithms that share a common API; it means that it has been designed for flexibility and extensibility in order to facilitate research projects that involve new or unusual use cases. In this paper we give a brief history of the library, an overview of its basic philosophy, a summary of the library's architecture, and a description of how the Pylearn2 community functions socially.
years
2014 3verdicts
ACCEPT 3representative citing papers
NICE learns a composition of invertible neural-network layers that transform data into independent latent variables, enabling exact log-likelihood training and sampling for density estimation.
Conditional GANs generate samples matching a given condition by supplying the condition to both generator and discriminator.
citing papers explorer
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Generative Adversarial Networks
A generative model is trained to match a data distribution by competing in a minimax game against a discriminator, reaching an equilibrium where the generator recovers the true distribution and the discriminator outputs 1/2 everywhere.
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NICE: Non-linear Independent Components Estimation
NICE learns a composition of invertible neural-network layers that transform data into independent latent variables, enabling exact log-likelihood training and sampling for density estimation.
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Conditional Generative Adversarial Nets
Conditional GANs generate samples matching a given condition by supplying the condition to both generator and discriminator.