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In machine learning, the Variational Autoencoder (VAE) is an artificial neural network structure proposed by Diederik P. Kingma and Max Welling, which belongs to the probabilistic graphical model and variational Bayesian method.

It is a generative model specifically designed to capture the underlying probability distribution of a given dataset and generate new samples.They utilize an architecture that contains an encoder-decoder structure. The encoder transforms the input data into a latent form, and the decoder aims to reconstruct the original data based on this latent representation. VAEs are programmed to minimize the difference between the original and reconstructed data, allowing them to understand the underlying data distribution and generate new samples that conform to the same distribution.

A significant advantage of VAEs is their ability to generate new data samples that are similar to the training data. Since the latent space of VAEs is continuous, the decoder can generate new data points that interpolate seamlessly between the training data points. VAEs have applications in various fields such as density estimation and text generation.

Architecture of Variational Autoencoder

VAEs typically have two main components: encoder connections and decoder connections. The encoder network transforms the input data into a low-dimensional secret space, often called a “secret code.”

Various neural network topologies (e.g. fully connected neural networks or convolutional neural networks) can be studied to implement the encoder network. The architecture chosen is based on the characteristics of the data. The encoder network produces the basic parameters required to sample and generate the latent code, such as the mean and variance of the Gaussian distribution.

Similarly, researchers can use various types of neural networks to build a decoder network, whose goal is to reconstruct the original data from the provided latent code.

References

【1】https://zh.wikipedia.org/wiki/%E5%8F%98%E5%88%86%E8%87%AA%E7%BC%96%E7%A0%81%E5%99%A8

【2】https://www.analyticsvidhya.com/blog/2023/07/an-overview-of-variational-autoencoders/