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Beta-VAE has 2 Behaviors: PCA or ICA?
Beta-VAE is a very classical model for disentangled representation learning, the use of an expanding bottleneck that allow information into the decoder gradually is key to representation disentanglement as well as high-quality reconstruction.
Learning Stable Representations with Full Encoder
While the beta-VAE family is aiming to find disentangled representations and acquire human-interpretable generative factors, like what an ICA (from the linear domain) does, we propose Full Encoder, a novel unified autoencoder framework as a correspondence to PCA in the non-linear domain.
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