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Introduction | Machine Learning | Google for Developers Generative adversarial networks (GANs) are an exciting recent innovation in machine learning GANs are generative models: they create new data instances that resemble your training data For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person
Overview of GAN Structure | Machine Learning - Google Developers A generative adversarial network (GAN) has two parts: The generator learns to generate plausible data The generated instances become negative training examples for the discriminator The discriminator learns to distinguish the generator's fake data from real data The discriminator penalizes the generator for producing implausible results When training begins, the generator produces
GAN Variations | Machine Learning | Google for Developers Text-to-image GANs take text as input and produce images that are plausible and described by the text For example, the flower image below was produced by feeding a text description to a GAN
The Discriminator | Machine Learning | Google for Developers The discriminator in a GAN is simply a classifier It tries to distinguish real data from the data created by the generator It could use any network architecture appropriate to the type of data it's classifying Figure 1: Backpropagation in discriminator training Discriminator Training Data The discriminator's training data comes from two sources: Real data instances, such as real pictures
Introdução | Machine Learning | Google for Developers As redes adversárias generativas (GANs, na sigla em inglês) são uma inovação recente e interessante no aprendizado de máquina As GANs são modelos generativos: elas criam novas instâncias de dados que se assemelham aos dados de treinamento
GAN Training | Machine Learning | Google for Developers It's this back and forth that allows GANs to tackle otherwise intractable generative problems We get a toehold in the difficult generative problem by starting with a much simpler classification problem
Visão geral da estrutura da GAN - Google Developers Uma rede adversarial generativa (GAN) tem duas partes: O gerador aprende a gerar dados plausíveis As instâncias geradas se tornam exemplos de treinamento negativos para o discriminador O discriminador aprende a distinguir os dados falsos do gerador dos dados reais O discriminador penaliza o gerador por produzir resultados implausíveis Quando o treinamento começa, o gerador produz dados