Generative Adversarial Networks (GAN)

A GAN's generator maps a source of variation, often random latent inputs, into generated examples. A discriminator learns to distinguish generated examples from observed data, and its feedback shapes generator updates. Conditional variants incorporate labels or other guidance. The two components change together, creating optimization dynamics unlike a single fixed supervised objective. A generator can produce convincing samples while ignoring parts of the distribution, a failure often described as mode collapse. Competence includes understanding the objective variant and architecture rather than treating every adversarial method as equivalent. A discriminator score is part of the training game, not automatically a reliable external quality metric.

Choose a data representation and objective suited to the generation task, and verify the update sequence for generator and discriminator. Monitor training dynamics and sample diversity across latent inputs, comparing multiple runs where feasible. Evaluate held-out fidelity and coverage with appropriate quantitative measures and human inspection. Record conditioning and sampling procedures. The deliverable is a reproducible generator with evidence about its output distribution and failure modes, including whether apparent quality comes from broad learning or a narrow set of repeated patterns.

Adversarial training can be unstable and sensitive to architecture and optimization choices. Mode collapse, memorization and artifacts may not be obvious in a few selected samples. Quality metrics have their own representation assumptions and should not be read as universal perceptual scores. GANs differ from diffusion models and variational autoencoders in objective and sampling behavior. Evaluate diversity, fidelity and training reproducibility separately, and avoid claims that a convincing discriminator or image proves complete distributional learning.