Скачать или смотреть 14 Understanding Generative Adversarial Networks GANs

Generative Adversarial Networks represent a major advance in synthetic data generation. GANs operate through an adversarial training approach involving two competing neural networks: the Generator and the Discriminator. The Generator attempts to create realistic synthetic data from random noise, while the Discriminator acts as a binary classifier attempting to distinguish between the real data and the samples created by the Generator. This constant competition, visualized as a min-max game, drives both networks to improve, ultimately enabling the Generator to produce high-fidelity output.

GANs are applied across various fields, including generating photorealistic images, enhancing image resolution, generating synthetic data, and performing image-to-image translation. A practical implementation example illustrates the creation and joint training of a simple Generator and Discriminator using PyTorch to generate handwritten digits from the MNIST dataset, highlighting how maintaining an appropriate balance between the two networks is essential for successful and stable learning.