The paper proposes a PCA-based method to find latent directions for each generative factor, creating disentanglement metrics that work for latent variable models with non-axis-aligned encodings, and reports improved scores on DSprites and 3D Shapes.
Stochastic backprop- agation and approximate inference in deep generative models,
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Disentanglement Analysis in Deep Latent Variable Models Matching Aggregate Posterior Distributions
The paper proposes a PCA-based method to find latent directions for each generative factor, creating disentanglement metrics that work for latent variable models with non-axis-aligned encodings, and reports improved scores on DSprites and 3D Shapes.