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.
Understanding GAN
A GAN, or generative adversarial network, is a deep learning architecture consisting of two competing neural networks: a generator that creates synthetic data and a discriminator that evaluates whether samples are real or fake. Through this adversarial training process, both networks improve iteratively, with the generator learning to produce increasingly realistic outputs. GANs have been applied to image synthesis, style transfer, data augmentation, deepfake creation, super-resolution imaging, and drug molecule design. Notable variants include StyleGAN for high-fidelity face generation, CycleGAN for unpaired image-to-image translation, and conditional GANs that generate outputs based on specified attributes. Training GANs can be challenging due to issues like mode collapse and training instability, which researchers address through architectural innovations and specialized loss functions like Wasserstein distance. GANs remain a cornerstone of generative AI despite growing competition from diffusion models.
Category
Generative AI
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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.
Gemini
Gemini is Google's family of multimodal AI models capable of processing text, images, audio, and video. It represents Google's most advanced AI system and competes with models like GPT-4 and Claude.