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[D] Can someone help me understand the latent encoding space of a variational autoencoder?

So I trained a VAE on 1-D Sparse data, and I am attempting to use the encoded latent variables for a similarity metric. However, the latent space has an extra dimension that I have no idea where it came from, and I am not sure which variable to use. I am attempting to use z_mean as my latent variables. But the shape of the output from the z_mean layer is somehow (8*512), even though my latent size was 512. Can someone help me understand what is going on here? Thank you!

submitted by /u/that_one_ai_nerd
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.