Contrastive Analysis VAE (CA-VAEs) is a family of Variational auto-encoders (VAEs) that aims at separating the common factors of variation between a background 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 both datasets). Currently, all models fail to prevent the sharing of information between latent spaces effectively and to capture all salient factors of variation. To this end, we introduce two crucial regularization losses: a disentangling term between common and salient representations and a classification term between background and target samples in the salient space. We show a better performance than previous CA-VAEs methods on three medical applications and a natural images dataset (CelebA). Code and datasets are available on GitHub https://github.com/neurospin-projects/2023_rlouiset_sepvae.

该论文提出Contrastive Analysis VAE (CA-VAEs)方法来解决VAE中存在的信息共享和捕捉所关注的变化因素的问题，并使用两个关键的规则化损失（common-salient表示的分离项和在salient空间中对背景和目标样本的分类项）进行改进。实验证明CA-VAEs在医学应用和自然图像数据集CelebA上的表现优于先前的方法。

SepVAE：一种对比VAE用于将病态模式与健康模式分开