Autoencoder and Variational Autoencoder are unsupervised learning techniques in deep learning.
An autoencoder (AE) is a neural network that is trained to replicate its input data. It is an unsupervised learning algorithm that reduces the input data into a lower-dimensional code, and then reconstructs the original input data from this lower-dimensional representation. The autoencoder has an encoder, which compresses the input data into a lower-dimensional code, and a decoder, which then reconstructs the input data from this lower-dimensional representation. Autoencoders are widely used in image and text data compression, feature learning, and anomaly detection.
On the other hand, a Variational Autoencoder (VAE) is a type of autoencoder that is able to learn the underlying probability distribution of the input data, and use it to generate new, similar data samples. In VAE, the low-dimensional code generated by the encoder follows a specific probability distribution, typically a Gaussian distribution. A VAE learns to map the input data into this probability distribution, rather than a fixed point, and then it samples from this distribution to generate new data. By doing so, the VAE is able to generate new data, similar to the input data, but with small variations or modifications. Variational Autoencoders are commonly used in applications such as image and text generation, semisupervised learning, and data augmentation.
The main difference between Autoencoder and Variational Autoencoder is that Autoencoder seeks to find a fixed-point in the lower-dimensional representation, whereas Variational Autoencoder discovers a probability distribution in the lower-dimensional space.
Autoencoders are used for reduction of data dimensionality, feature learning from data, and anomaly detection. Variational Autoencoder’s main use case is generation of new data samples while preserving the original data distribution.
In summary, Autoencoders and Variational Autoencoders are important unsupervised learning techniques that have brought new applications in data compression, image reconstruction, and generation of new data. While both types of models can be used to find interesting low-dimensional representations of the input data, Variational Autoencoder has an edge in modeling probability distributions, and thus is better suited for data generation applications.