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GAN (Generative Adversarial Network)

Model

Definition

An architecture with two networks — a generator that creates synthetic data and a discriminator that tries to distinguish real from fake. Training as an adversarial game pushes the generator toward photorealistic output. Largely superseded by diffusion models for images.

Related Terms

Diffusion Model

A generative model that learns to reverse a gradual noising process. Starting from pure noise, the model iteratively denoises to produce images, audio, or video. Powers Stable Diffusion, DALL-E 3, Midjourney, and Sora.

All Terms

AgentAttention MechanismChain-of-ThoughtContext WindowDiffusion ModelDistillationDPO (Direct Preference Optimization)EmbeddingFew-shot PromptingFine-tuningFunction CallingGAN (Generative Adversarial Network)GRPO (Group Relative Policy Optimization)HallucinationInferenceLLM (Large Language Model)LoRA (Low-Rank Adaptation)MCP (Model Context Protocol)MultimodalPrompt EngineeringQLoRA (Quantized LoRA)QuantizationRAG (Retrieval-Augmented Generation)RLHF (Reinforcement Learning from Human Feedback)TemperatureTokenizerTool UseTop-p (Nucleus Sampling)TransformerVector DatabaseZero-shot Prompting