Chapter 11: Generative Models in PyTorch
Abstract : Generative models in PyTorch are a class of deep learning models designed to create new data instances that resemble the training data. PyTorch, a flexible deep learning framework, provides the tools and functionalities necessary to implement various types of generative models. Common Generative Models Implemented in PyTorch: Generative Adversarial Networks (GANs): GANs consist of two neural networks: a generator and a discriminator. The generator learns to produce synthetic data (e.g., images) from a random noise vector. The discriminator learns to distinguish between real data and the synthetic data generated by the generator. They are trained in a competitive setup, where the generator aims to fool the discriminator, and the discriminator aims to accurately identify fakes. Variational Autoencoders (VAEs): VAEs are a type of autoencoder that learn a probabilistic mapping from the input data to a latent space. They aim to generate new data by sampling from t...