generative adversarial networks
Generative adversarial networks (GANs) are a type of machine learning framework consisting of two neural networks, the generator and the discriminator, that work in an adversarial manner. The generator generates new data samples, such as images, while the discriminator tries to differentiate between the generated samples and real samples from the training dataset. Through iterative training, GANs learn to generate increasingly realistic data by improving both the generator's ability to generate realistic samples and the discriminator's ability to accurately distinguish between real and generated samples. The generator and the discriminator are trained simultaneously, fostering a competitive learning process that drives the overall improvement of the model's generative capabilities.
Requires login.
Related Concepts (1)
Similar Concepts
- adversarial autoencoders
- adversarial deep learning
- adversarial image classification
- adversarial image synthesis
- adversarial input synthesis
- adversarial machine learning
- adversarial networks
- adversarial reinforcement learning
- adversarial text generation
- generalized adversarial loss
- generative adversarial network (gan) layers
- generative adversarial networks (gan)
- generative adversarial networks (gans)
- generative adversarial networks with attention
- generative models