ControlVAE is a variational autoencoder (VAE) framework that combines the automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) control, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows for control of the KL-divergence to a desired value (set point), which is effective in avoiding posterior collapse and learning disentangled representations.
Source: ControlVAE: Tuning, Analytical Properties, and Performance AnalysisPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Disentanglement | 2 | 25.00% |
Image Generation | 2 | 25.00% |
Language Modeling | 2 | 25.00% |
Language Modelling | 2 | 25.00% |
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