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Fig. 1 | BMC Bioinformatics

Fig. 1

From: A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis

Fig. 1

The novel architecture of an Adversarial Variational AutoEncoder with Dual Matching (AVAE-DM). An autoencoder (that is, a deep encoder and a deep decoder) reconstructs the scRNA-seq data from a latent code vector z. The first discriminator network D1 is trained to discriminatively predict whether a sample arises from a sampled distribution or from the latent distribution of the autoencoder. The second discriminator D2 is trained to discriminatively predict whether the scRNA-seq data is real or fake

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