{"id":36343,"date":"2024-11-01T09:47:40","date_gmt":"2024-11-01T09:47:40","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36343"},"modified":"2024-11-01T11:00:18","modified_gmt":"2024-11-01T11:00:18","slug":"creating-gan-deep-learning-and-vae-using-pytorch","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36343\/","title":{"rendered":"Creating GAN Deep Learning and VAE Using PyTorch"},"content":{"rendered":"

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1. Introduction<\/h2>\n

The advancements in artificial intelligence have increased the importance of Generative Models. Generative models play a role in generating data that is structurally different from each other, with GAN (Generative Adversarial Networks) and VAE (Variational Autoencoder) being widely used. This article will detail how to implement GAN and VAE using PyTorch.<\/p>\n<\/div>\n

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2. GAN (Generative Adversarial Networks)<\/h2>\n

GAN is a model proposed by Ian Goodfellow in 2014, where two neural networks (the generator and the discriminator) compete against each other during training. The generator creates fake data while the discriminator is responsible for distinguishing between real and fake data.<\/p>\n

2.1 Structure of GAN<\/h3>\n

GAN consists of the following structure:<\/p>\n