Variational Autoencoders (VAEs) are a type of artificial neural network used for unsupervised learning. They consist of two main parts: an encoder that compresses input data into a lower-dimensional representation, and a decoder that reconstructs the original data from this representation. VAEs are particularly useful for generating new data samples similar to the training data. The key feature of VAEs is their use of probabilistic methods. Instead of mapping inputs to fixed points in the latent space, they model the data distribution by learning parameters of a probability distribution. This allows VAEs to generate diverse outputs, making them valuable in fields like image generation and natural language processing.