  One major challenge of disentanglement learning with variational autoencoders
is the trade-off between disentanglement and reconstruction fidelity. Previous
studies, which increase the information bottleneck during training, tend to
lose the constraint of disentanglement, leading to the information diffusion
problem. In this paper, we present a novel framework for disentangled
representation learning, DeVAE, which utilizes hierarchical latent spaces with
decreasing information bottlenecks across these spaces. The key innovation of
our approach lies in connecting the hierarchical latent spaces through
disentanglement-invariant transformations, allowing the sharing of
disentanglement properties among spaces while maintaining an acceptable level
of reconstruction performance. We demonstrate the effectiveness of DeVAE in
achieving a balance between disentanglement and reconstruction through a series
of experiments and ablation studies on dSprites and Shapes3D datasets. Code is
available at https://github.com/erow/disentanglement_lib/tree/pytorch#devae.
