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Unsupervised Geometric Disentanglement via CFAN-VAE
Geometric disentanglement, the separation of latent codes for intrinsic (i. e. identity) and extrinsic (i. e. pose) geometry, is a prominent task for generative models of non-Euclidean data such as 3D deformable models.
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics
Empowered by the disentangled latent space learning, the extrinsic latent embedding is successfully used for classification or property prediction of different drugs bound to a specific protein.
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