What are VAEs?
A variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It is part of the families of probabilistic graphical models and variational Bayesian methods. VAEs are generative models explicitly designed to capture the underlying probability distribution of a given dataset and generate novel samples. They utilize an architecture that comprises an encoder-decoder structure. A VAE provides a probabilistic manner for describing an observation in latent space.
Generative Models in AI: A Comprehensive Comparison of GANs and VAEs
The world of artificial intelligence has witnessed a significant surge in the development of generative models, which have revolutionized the way we approach tasks like image and video generation, data augmentation, and more. Among the most popular and widely used generative models are Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
GANs consist of a generator and a discriminator network that compete against each other in a two-player minimax game. The generator tries to generate realistic samples from random noise, while the discriminator aims to distinguish between real and fake samples. On the other hand, VAEs are probabilistic models that learn a latent representation of the input data. In this article, we’ll delve into the intricacies of GANs and VAEs, exploring their key differences, similarities, and real-world applications.
Table of Content
- Understanding Generative Models
- What are GANs?
- What are VAEs?
- Key Differences Between GANs and VAEs
- Training Process for GANs
- Advantages and Disadvantages of GANs
- Applications of GANs
- Training Process for VAEs
- Advantages and Disadvantages of VAEs
- Applications of VAEs
- Similarities Between GANs and VAEs
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