Generative Adversarial Network
A Generative Adversarial Network is a deep learning framework where two neural networks compete: a generator creates synthetic data while a discriminator evaluates authenticity. This adversarial process produces remarkably realistic outputs.
Understanding Generative Adversarial Network
A generative adversarial network (GAN) is a framework in which two neural networks, a generator and a discriminator, are trained simultaneously through an adversarial process. The generator learns to produce synthetic data samples that resemble real data, while the discriminator learns to tell real and generated samples apart. This competitive dynamic, formalized as a minimax optimization problem and solved through gradient descent, pushes the generator to create increasingly convincing outputs. GANs have been used to generate photorealistic human faces, create artwork, perform image-to-image translation, enhance low-resolution images, and produce training data through data augmentation. They also enable deepfake technology and medical image synthesis. Challenges in GAN training include mode collapse, where the generator produces limited variety, and training instability. Variants like Wasserstein GAN, StyleGAN, and conditional GAN address these issues and expand the architecture's applicability across generative AI.
Category
Generative AI
Is AI recommending your brand?
Find out if ChatGPT, Perplexity, and Gemini mention you when people search your industry.
Check your brand — $9Related Generative AI Terms
Chain of Thought
Chain of thought is a prompting technique that encourages large language models to break down complex reasoning into intermediate steps. This approach significantly improves performance on math, logic, and multi-step reasoning tasks.
ChatGPT
ChatGPT is an AI chatbot developed by OpenAI that uses large language models to generate human-like conversational responses. It became one of the fastest-growing consumer applications in history after its launch in November 2022.
Claude
Claude is an AI assistant developed by Anthropic, designed to be helpful, harmless, and honest. It is built using Constitutional AI techniques and competes with models like GPT-4 and Gemini.
Diffusion Model
A diffusion model is a generative AI model that creates data by learning to reverse a gradual noise-adding process. Diffusion models power state-of-the-art image generation systems like Stable Diffusion and DALL-E.
Discriminator
A discriminator is the component of a GAN that learns to distinguish between real and generated data. It provides feedback to the generator, creating an adversarial training dynamic that improves output quality.
Few-Shot Prompting
Few-shot prompting provides a language model with a small number of input-output examples in the prompt to demonstrate the desired task format. This technique helps models understand task requirements without any fine-tuning.
Foundation Model
A foundation model is a large AI model trained on broad data that can be adapted to a wide range of downstream tasks. GPT-4, Claude, Gemini, and DALL-E are examples of foundation models that serve as bases for specialized applications.
GAN
A GAN (Generative Adversarial Network) is a generative model consisting of two competing neural networks — a generator and a discriminator. GANs produce realistic synthetic data by training these networks in an adversarial game.