Variational Autoencoder (VAE)
A Variational Autoencoder (VAE) is a type of neural network that combines autoencoders with probabilistic modeling. Unlike traditional autoencoders, which simply compress and reconstruct data, VAEs learn a latent probability distribution. This allows them not only to recreate inputs but also to generate entirely new samples, making them a cornerstone of generative AI.
Background and origins
Proposed by Kingma and Welling in 2013, VAEs were designed to overcome a key limitation of autoencoders: the lack of a smooth and structured latent space. By imposing a Gaussian prior over the latent variables, VAEs create a continuous and interpretable latent space, making sampling and generation feasible and stable.
Practical applications
- Image generation: producing synthetic faces, objects, or artworks.
- Speech and music: generating realistic audio samples by modeling complex distributions of sound.
- Healthcare: simulating patient records or MRI scans to support AI training without compromising privacy.
- Data compression: creating low-dimensional representations of complex datasets for visualization or clustering.
Challenges and debates
VAEs often face a trade-off between sample sharpness and diversity. While they offer theoretical elegance and stability, their generated outputs are sometimes blurrier compared to GANs. However, VAEs remain highly valued for their probabilistic structure, interpretability, and ability to provide insights into the underlying data distribution.
