What are "Variational Autoencoders" (VAEs)?

Variational Autoencoders (VAEs) are indeed generative models designed to learn representations of input data through the processes of encoding and decoding. They work by encoding input data into a latent space, representing the underlying factors of variation, and then decoding this representation to reconstruct the original input. This two-stage process allows VAEs to capture the underlying distribution of the input data, making them effective for generating new data that resembles the training data.

The unique feature of VAEs is the use of variational inference to approximate the posterior distribution of the latent variables, enabling the model to generate new data points by sampling from this distribution. This distinguishes VAEs from traditional autoencoders, which do not incorporate a probabilistic approach and do not learn a distribution over the latent space.

The other options describe different concepts that do not align with the characteristics and functionalities of VAEs. For instance, VAEs are not specifically designed for audio processing, nor are they used for supervised learning tasks like classification or mainly focused on data preprocessing. This clarifies why the selected answer accurately describes the role and purpose of Variational Autoencoders.