Editing Gans (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Generator (G)''' — A neural network that takes a random noise vector as input and produces synthetic data (images, audio, etc.) designed to fool the discriminator.
* '''Discriminator (D)''' — A neural network that takes a data sample (real or generated) as input and outputs a probability that it is real (not fake).
* '''Latent space''' — The space of random noise vectors (z) that the generator maps to data space. Interpolating in latent space produces smooth transitions between generated samples.
* '''Adversarial training''' — The min-max game between G and D: G minimizes and D maximizes the same loss function simultaneously.
* '''Nash equilibrium''' — The theoretical ideal endpoint of GAN training, where G generates samples indistinguishable from real data and D outputs 0.5 for all inputs.
* '''Mode collapse''' — A common GAN failure where the generator learns to produce only a small variety of outputs, ignoring most of the real data distribution.
* '''Training instability''' — GANs are notoriously difficult to train; the generator and discriminator can fail to converge or collapse.
* '''DCGAN (Deep Convolutional GAN)''' — An early influential GAN using convolutional layers; established architectural best practices.
* '''Conditional GAN (cGAN)''' — A GAN conditioned on additional information (class label, image, text) to control what is generated.
* '''StyleGAN''' — A high-quality face generation GAN (NVIDIA) known for its disentangled latent space and photorealistic outputs.
* '''CycleGAN''' — A GAN for unpaired image-to-image translation (e.g., photos ↔ paintings) without paired training examples.
* '''Pix2Pix''' — A conditional GAN for paired image-to-image translation (e.g., sketches → photos, day → night).
* '''Wasserstein GAN (WGAN)''' — A GAN variant using Wasserstein distance as the loss, dramatically improving training stability.
* '''FID (Fréchet Inception Distance)''' — The standard metric for GAN image quality; measures the distance between real and generated image distributions.
* '''Progressive growing''' — A training technique where the resolution of generated images increases gradually during training.
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