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eyeswideshhh t1_iurafh8 wrote

Denoising/vanilla autoencoder does not impose any constraints on latent representation of encoder and thus may have highly entangled fearures , you can verify this with clustering.

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Dear-Vehicle-3215 OP t1_iuriai1 wrote

I will try to check this, I didn't think about it.

The only way Clustering came to my mind was for extracting some clusters and then using them as a label to evaluate my features.

I am trying also to implement a penalization in the latent representation by applying the norm of the jacobian matrix, but since I see no one using it, I was thinking that was wrong in Convolutional AE (and also the paper use a different definition from the one that I am using).

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eyeswideshhh t1_iurj9lt wrote

I have never heard of this method, you can also try beta-VAE and joint-VAE

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Dear-Vehicle-3215 OP t1_iuvdyog wrote

Thank you very much. It seems that VAE could be a nice choice for me.

Anyway, by plotting the cluster map it seems that there are several features higly correlated, but also a lots of feature with low correlation

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