Generative adversarial network (GAN)
- See also: Machine learning terms
Introduction
A Generative Adversarial Network (GAN) is a type of machine learning algorithm developed by Ian Goodfellow and his colleagues in 2014[1]. GANs are comprised of two distinct neural networks, the generator and the discriminator, which compete with each other in a game-theoretic framework. GANs have been used to generate a wide range of data types, including images, audio, and text, and have demonstrated promising results in various applications, such as image synthesis, data augmentation, and unsupervised learning.
Architecture
GANs consist of two primary components: the generator and the discriminator. These components, typically implemented as deep learning models, work in tandem to achieve a generative process.
Generator
The generator is responsible for producing synthetic data samples. It takes random noise as input, which is typically represented as a latent vector, and maps it to the desired data distribution using a series of transformations. The objective of the generator is to create data samples that are indistinguishable from the real data distribution.
Discriminator
The discriminator acts as a binary classifier, determining whether a given data sample is real (i.e., from the actual data distribution) or fake (i.e., generated by the generator). The objective of the discriminator is to accurately differentiate between real and generated samples, thus improving its classification accuracy.
Training Process
GANs are trained in a minimax game setting, in which the generator and discriminator are pitted against each other. The generator aims to maximize the probability that the discriminator misclassifies its generated samples as real, while the discriminator aims to minimize this probability. This adversarial training process can be represented by the following value function:
V(G, D) = Ex∼Pdata(x)[log D(x)] + Ez∼Pz(z)[log(1−D(G(z)))]
During the training process, the generator and discriminator are updated alternately. The discriminator is updated by minimizing its loss function, which encourages it to correctly classify real and generated samples. The generator is updated by maximizing its loss function, which encourages it to create samples that the discriminator cannot distinguish from real data.
Challenges and Limitations
Despite their impressive results, GANs face several challenges and limitations, including mode collapse, instability during training, and sensitivity to hyperparameters.
Mode Collapse
Mode collapse occurs when the generator fails to capture the full diversity of the real data distribution and instead generates only a small subset of samples. This can lead to the generated samples lacking variety and not accurately representing the target distribution.
Training Instability
Training GANs can be unstable due to the competing objectives of the generator and discriminator. This can result in oscillations in the training process, where the performance of the generator and discriminator fluctuate without converging to a stable equilibrium.
Hyperparameter Sensitivity
GANs are sensitive to the choice of hyperparameters, such as learning rates and network architectures. The performance of GANs can vary significantly based on these choices, making it difficult to find optimal configurations.
Explain Like I'm 5 (ELI5)
Imagine you're playing a game with
- ↑ Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680).