GAN (Generative Adversarial Network)
Two networks trained against each other — one faking, one detecting — until the fakes pass. The technique diffusion largely replaced.
When not to use it
- For general image generation today. Diffusion models are better, more diverse, and vastly less painful to train. Choose a GAN only for a specific reason.
- When you need output diversity. Mode collapse is not an edge case; it's the characteristic failure, and it produces confident sameness.
- When you can't evaluate by looking. There's no loss value that means "good," and the automated metrics are proxies you shouldn't trust alone.
Reach for something else instead
- Diffusion models — the default for image generation now: stable training, better coverage of the data distribution.
- VAEs when you want a well-behaved latent space and can accept blurrier output.
- Distilled diffusion if what you actually wanted was GAN-like speed with diffusion quality.
Sources & further reading
- Goodfellow et al. (2014), Generative Adversarial Nets — the original, and unusually readable.
- Karras et al. (2018), A Style-Based Generator Architecture for Generative Adversarial Networks — StyleGAN, the peak of GAN image quality.
- Arjovsky, Chintala & Bottou (2017), Wasserstein GAN — the most influential attempt to make training stable, and a clear account of why it wasn't.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Reading a falling generator loss as progress. In an adversarial game, loss values are relative to an opponent that's also moving. They mean much less than they appear to.
- Fighting mode collapse with more training. It's a failure of the objective, not of patience.
- Trusting FID as ground truth. It's sensitive to implementation details and rewards things human viewers don't care about.
At a glance
FieldGenerative AI
Core ideagenerator vs. discriminator
Introduced2014, Goodfellow et al.
Strengthsingle-pass speed
Weaknessunstable training, mode collapse
DifficultyAdvanced
Flashcards for this concept
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Often compared with
GAN vs. diffusion — one fast pass from an unstable contest vs. many steady steps from a stable objective.