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Variational autoencoders (VAEs) have recently been used for unsuperviseddisentanglement learning of complex density distributions. Numerous variantsexist to encourage disentanglement in latent space while improvingreconstruction. However, none have simultaneously managed the trade-off betweenattaining extremely low reconstruction error and a high disentanglement score.We present a generalized framework to handle this challenge under constrainedoptimization and demonstrate that it outperforms state-of-the-art existingmodels as regards disentanglement while balancing reconstruction. We introducethree controllable Lagrangian hyperparameters to control reconstruction loss,KL divergence loss and correlation measure. We prove that maximizinginformation in the reconstruction network is equivalent to informationmaximization during amortized inference under reasonable assumptions andconstraint relaxation.

GCVAE: Generalized-Controllable Variational AutoEncoder