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Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders(VAEs) that aims at separating the common factors of variation between abackground dataset (BG) (i.e., healthy subjects) and a target dataset (TG)(i.e., patients) from the ones that only exist in the target dataset. To do so,these methods separate the latent space into a set of salient features (i.e.,proper to the target dataset) and a set of common features (i.e., exist in bothdatasets). Currently, all models fail to prevent the sharing of informationbetween latent spaces effectively and to capture all salient factors ofvariation. To this end, we introduce two crucial regularization losses: adisentangling term between common and salient representations and aclassification term between background and target samples in the salient space.We show a better performance than previous CA-VAEs methods on three medicalapplications and a natural images dataset (CelebA). Code and datasets areavailable on GitHub https://github.com/neurospin-projects/2023_rlouiset_sepvae.

SepVAE: a contrastive VAE to separate pathological patterns from healthy ones