Generative AI Models
Generative models are designed to generate new data instances that resemble your training data, enabling machines to learn the distribution of data points and generate similar items. They’re crucial in fields requiring new content generation or feature enhancement.
Common Algorithms:
- Generative Adversarial Networks (GANs): Use a dual-network architecture of generators and discriminators to improve each other.
- Variational Autoencoders (VAEs): Provide a probabilistic way to describe an observation in latent space.
When to Use:
- When you need to enhance, increase, or generate new data samples from existing data.
- Suitable for tasks like data augmentation, synthetic data generation for training models, and creative content generation.
- Using GANs to create realistic and infinite new environments for video game development.
- Utilizing VAEs to generate new clothing items based on current fashion trends.
Real-Life Application:
- Film and Video: Film companies use GANs to enhance the resolution of old movies or generate realistic special effects for new productions without the need for expensive practical effects.
Common AI Models and When to use them?
Artificial intelligence (AI) models include a wide range of techniques and approaches designed to mimic human thought processes and perform tasks that would typically need human intervention. These models vary widely in their applications, complexity, and the specific tasks they are designed to perform, each suited to different aspects of professional and everyday life.
In this article, we will explore various types of Artificial Intelligence Models, their characteristics, and their uses in the rapidly evolving area of modern technology.