Learning What and Where to Draw: Difference between revisions

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== Introduction ==
== Introduction ==


Recently Generative Adversarial Networks (GANs) have been highly successful in several machine learning applications. Specifically, these models have been successfully used by to synthesize real-world images. GANS consist of two components: a generator network and a discriminator network. The objective of the generator is to synthesize images that the discriminator will classify as real. In turn, the objective of the discriminator is to classify it's input as synthetic or real.   In what follows I outline the contents of the paper 'Learning What and Where to Draw' by Akata et al. (2016).
Generative Adversarial Networks (GANs) have been successfully used to synthesize compelling real-world images. In what follows we outline an enhanced GAN called the Generative Adversarial What- Where Network (GAWWN). In addition to accepting as input a noise vector, this network also accepts as input instructions describing what content to draw and in which location to draw the content. Traditionally, these models use simply conditioning variables such as a class label or a non-localized caption. The authors of 'Learning What and Where to Draw'

Revision as of 17:49, 17 October 2017

Introduction

Generative Adversarial Networks (GANs) have been successfully used to synthesize compelling real-world images. In what follows we outline an enhanced GAN called the Generative Adversarial What- Where Network (GAWWN). In addition to accepting as input a noise vector, this network also accepts as input instructions describing what content to draw and in which location to draw the content. Traditionally, these models use simply conditioning variables such as a class label or a non-localized caption. The authors of 'Learning What and Where to Draw'

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