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One major challenge of disentanglement learning with variational autoencodersis the trade-off between disentanglement and reconstruction fidelity. Previousstudies, which increase the information bottleneck during training, tend tolose the constraint of disentanglement, leading to the information diffusionproblem. In this paper, we present a novel framework for disentangledrepresentation learning, DeVAE, which utilizes hierarchical latent spaces withdecreasing information bottlenecks across these spaces. The key innovation ofour approach lies in connecting the hierarchical latent spaces throughdisentanglement-invariant transformations, allowing the sharing ofdisentanglement properties among spaces while maintaining an acceptable levelof reconstruction performance. We demonstrate the effectiveness of DeVAE inachieving a balance between disentanglement and reconstruction through a seriesof experiments and ablation studies on dSprites and Shapes3D datasets. Code isavailable at https://github.com/erow/disentanglement_lib/tree/pytorch#devae.

Variantional autoencoder with decremental information bottleneck for disentanglement