Autoencoders are a type of neural network used for unsupervised learning and dimensionality reduction. They were first introduced in the 1980s, but have become more popular in recent years with the advent of deep learning.
An autoencoder consists of two parts: an encoder and a decoder. The encoder takes the input data and maps it to a lower-dimensional representation, while the decoder takes the lower-dimensional representation and maps it back to the original input space. The goal of the autoencoder is to learn a compressed representation of the input data that can be used for tasks such as data compression, denoising, and image generation.
Autoencoders can be trained using backpropagation, where the difference between the input data and the output of the decoder is used to update the weights of the network. One popular variant of autoencoders is the denoising autoencoder, which is trained to reconstruct clean data from noisy inputs.
Another variant of autoencoders is the variational autoencoder (VAE), which is trained to generate new data samples from a latent space learned by the encoder. VAEs use a probabilistic approach to generate new samples by sampling from a learned distribution in the latent space. This makes them useful for tasks such as image generation and data synthesis.
Autoencoders have been applied to a wide range of applications, including image compression, anomaly detection, and natural language processing. They are also often used as pretraining models for deep learning tasks, where the learned representations can be transferred to downstream tasks such as classification and regression.