> ML_ALGORITHM // GENERATIVE-ADVERSARIAL-NETWORK-GAN_v1.0
Generative Adversarial Network (GAN)
Game-theoretic deep generative framework pitting a generator against a discriminator in a minimax duel to produce photorealistic synthetic data.
Adversarial Generative Networksdeep-generativeblack-boxlarge (>100k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(epochs * batch_size * (gen + disc))
Inference Complexity:O(gen_forward)
Hardware Profile
CPU Friendly:No
Requires GPU:Yes
Memory Footprint:high
Interpretability & Data
Interpretability Tier:black-box
Training Data Needs:large (>100k)
Interpretability Assessment
Generator synthesizes crisp, photorealistic outputs through game-theoretic competition.
Suitable Tasks & Supported Modalities
Suitable Tasks:
image generationimage to image translation
Supported Modalities:
image
Implementing Libraries
Foundational Literature
Generative Adversarial Nets (GAN)Ian J. Goodfellow, Jean Pouget-Abadie (2014) · Advances in Neural Information Processing Systems (NeurIPS)
Common Pitfalls & Warnings
- Mode collapse where generator produces only a tiny subset of plausible samples
- Vanishing gradients when discriminator trains too quickly and dominates the generator