Adding random noise to latent representation increase the accuracy in the autoencoder

[monk1337]

I am working on a graph autoencoder project, it consists of dense layers like this :

dense ([10, 756] )--> dense ( [10, 512] ) --> latent ( [ 10, 256] ) --> dense ( [10, 512] ) --> dense ([10, 756])
This is a very simple setup, and training it with mean square error loss i am getting accuracy ~0.79
I thought to experiment with variational autoencoder but it didn't work out well on this problem, But the strange thing is if I am adding noise in latent representation, it actually increases the accuracy to ~ 0.85

Adding noise in latent then setup is
dense ([10, 756] )--> dense ( [10, 512] ) --> latent ( [ 10, 256] ) + random_normal([ 10, 256] ) --> dense ( [10, 512] ) --> dense ([10, 756])
It's giving good accuracy but I want to know the theory behind it, I thought it's denoising auto-encoder but since in denosing auto-encoder we add noise in input samples not in latent space so we can't say it's denosing auto-encoder. How should I explain why it's increasing the accuracy.

I am not using kl-divergence or variational autoencoder, then how it's increasing?

Thank you!