Variational Autoencoders

Variational Autoencoders (VAEs) are generative models that learn to encode and decode high-dimensional data such as images, videos, and audio. VAEs are a type of neural network that can learn a low-dimensional representation of the input data, called the latent space, which captures the underlying structure of the data. VAEs are trained using a probabilistic approach that maximizes the lower bound of the log-likelihood of the data. This is achieved by minimizing the reconstruction error between the input data and the output of the decoder network, and by regularizing the latent space to follow a prior distribution, typically a Gaussian distribution. VAEs can be used for various tasks such as data compression, data generation, and data augmentation.
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