Generative Models

Sources:

• https://github.com/SixArm/ai-starter-guide/blob/main/topics/generative-models/full.md

Overview

Generative models are a class of machine learning models designed to learn and model the underlying distribution of a dataset. These models can generate new data points that resemble the original data and are useful for various tasks, including data synthesis, data augmentation, and generating new samples for creative purposes.

Digging Further

Generative models play a crucial role in the field of unsupervised learning, where the goal is to learn the inherent structure and patterns of the data without explicit labels. Here are some common types of generative models:

• Variational Autoencoders (VAEs): VAEs are neural network-based generative models that learn to encode data into a latent space and then decode it back to reconstruct the original data. VAEs impose constraints on the latent space to follow a specific distribution, usually a Gaussian distribution, enabling the generation of new data points.
• Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator, and a discriminator, which are trained in an adversarial manner. The generator creates new data samples, and the discriminator tries to distinguish between real and generated data. Over time, the generator improves its ability to generate realistic data by fooling the discriminator.
• Autoregressive Models: Autoregressive models model the probability distribution of a sequence of data by making assumptions about the conditional probability of each data point given its predecessors. Examples include PixelCNN and WaveNet for image and audio generation, respectively.
• Normalizing Flows: Normalizing flows are a family of generative models that use invertible transformations to map data from a simple distribution to a more complex distribution. These transformations can be composed to generate complex data samples.
• Generative Moment Matching Networks (GMMNs): GMMNs use moment matching to match the moments of the generated distribution to the moments of the real data distribution. They have been used for various tasks, including image generation and data synthesis.
• Boltzmann Machines: Boltzmann Machines are a type of energy-based model that learns to capture the distribution of the data in terms of energy levels. They can be used for both generative and discriminative tasks.

Conclusion

Generative models have a wide range of applications, including image synthesis, text generation, style transfer, and data augmentation for training machine learning models. They are also employed in areas like art generation, video prediction, and drug discovery. However, training generative models can be challenging, and evaluating the quality of generated samples is an ongoing area of research. The field of generative models continues to advance rapidly, with new architectures and techniques constantly emerging to improve the fidelity and diversity of generated samples.

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Appendix

Note created on 2024-04-10 and last modified on 2024-04-10.

• Artificial Intelligence MOC
• Technology Map of Content
• Development Map of Content
• ChatGPT
• AI Tools
• OpenAI
• Anatomy of AI LLM-Based Agents
• Tool - Python - Langchain
• Chatbot

Backlinks

LIST FROM [[Generative Models]] AND -"CHANGELOG" AND -"00-INBOX/Generative Models"

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