{"id":36375,"date":"2024-11-01T09:47:58","date_gmt":"2024-11-01T09:47:58","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36375"},"modified":"2024-11-01T11:00:10","modified_gmt":"2024-11-01T11:00:10","slug":"gan-deep-learning-using-pytorch-what-is-generative-modeling","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36375\/","title":{"rendered":"GAN Deep Learning Using PyTorch, What is Generative Modeling?"},"content":{"rendered":"

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The advancement of deep learning is impacting various fields, and especially generative modeling is opening new horizons for data generation. Generative Adversarial Networks (GANs) are one of the most famous models in generative modeling, excelling in the ability to generate new data from raw data. This article aims to explain the main concepts of GAN, the implementation methods using PyTorch, and provide practical examples.<\/p>\n

1. Basics of GAN<\/h2>\n

GAN consists of two neural networks that serve the roles of a generator and a discriminator. These two networks are in an adversarial relationship and learn simultaneously.<\/p>\n

1.1 Generator<\/h3>\n

The generator’s role is to generate data that resembles real data from random noise (input noise). This involves learning the distribution of the data to create new data, with the goal of deceiving the discriminator.<\/p>\n

1.2 Discriminator<\/h3>\n

The discriminator’s role is to determine whether the input data is real or generated by the generator. This is also implemented as a neural network, and the discriminator’s goal is to accurately distinguish between real and fake data as much as possible.<\/p>\n

1.3 Adversarial Learning Process<\/h3>\n

The learning process of GAN consists of the following steps:<\/p>\n

    \n
  1. The generator produces data from random noise.<\/li>\n
  2. The discriminator receives real data and the fake data created by the generator and tries to distinguish between them.<\/li>\n
  3. The generator is optimized to trick the discriminator into misjudging fake data as real.<\/li>\n
  4. The discriminator is optimized to accurately distinguish fake data.<\/li>\n<\/ol>\n

    This process is repeated many times, gradually leading the generator to produce better data and the discriminator to make more refined judgments.<\/p>\n

    2. Structure of GAN<\/h2>\n

    GAN has the following structure.<\/p>\n