How deeply does Data Without Labels cover generative adversarial network?

Explains the generator, discriminator, adversarial training, applications, and major variants without detailing optimization mathematics or implementation code.

Generative adversarial network

As explained in Data Without Labels

A generative adversarial network is a generative model consisting of a generator and discriminator trained adversarially to produce realistic samples.

Generative adversarial networks (GANs) create synthetic data when applications need realistic new samples, expanded datasets, or reduced overfitting. They support uses including image generation, style transfer, and data augmentation. section 10.2 section 10.9

A GAN contains two neural networks trained adversarially. The generator creates synthetic data, while the discriminator distinguishes real data from generated data; their competition progressively improves realism. section 10.4 section 10.9

Choose variants according to the generation or transformation problem:

Conditional GANs use additional information, such as class labels, to control generated attributes. section 10.4.4
Deep convolutional GANs target high-quality image generation with convolutional layers. section 10.4.4
CycleGANs map images between domains for style transfer and image-to-image translation. section 10.4.4
BigGAN and StyleGAN generate high-resolution images while providing greater control over styles and attributes. section 10.4.4

Synthetic data for machine learning

A GAN can generate additional realistic samples to improve dataset quality and reduce overfitting risk, while image-focused variants address generation or domain-conversion tasks. section 10.9 section 10.4.4

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Related concepts

generatorhas part · A generator is a computational component or construct that produces data or values from inputs, representations, context, or specified constraints.
discriminatorhas part · A discriminator is the model component of a generative adversarial network that distinguishes real data from data generated by its generator.
VQGANincludes · VQGAN is a generative model that combines vector-quantized discrete latent representations with adversarial and perceptual objectives to synthesize images.
generative modelingkind of · Generative modeling is a machine-learning approach that learns patterns or a data distribution to generate new samples resembling the data.
DCGAN / deep convolutional ganincludes · A deep convolutional generative adversarial network is a GAN whose generator and discriminator use deep convolutional neural-network architectures.
generationkind of · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

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