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Disentangled representations aim to encode independent factors of variation (like shape, size, or color) into separate coordinates of a latent vector.
A Variational Autoencoder (VAE) is a probabilistic autoencoder that learns to generate data by inferring hidden causes (latent variables) and decoding them back to observations.