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Training convolutional variational autoencoders

Hi all.

Iam trying to train a convolutional variational autoencoder (CVAE) on computed tomography (CT) IMAGES (176X224 px) . The training data is normalized between 0 and 1 and Iam using approximately the same model structure as in keras autoencoder tutorial.

https://keras.io/examples/variational_autoencoder/

I only changed the depth and the size of the latent space to 128.

For the loss function I use Mse and KL, with a weight annealing for the KL part.

When I train the network it seems like it is learning something, but if I try to reconstruct images after training, the output images are just noisy.

I have no clue what it is Iam doing wrong.

Any advice would be really great.

Cheers,

M

submitted by /u/Mike_Sv86
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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.