From my understanding, the concrete autoencoder is an autoencoder, with the latent space constrained by concrete distribution, and now work as a feature selector.
I want to know if it's possible to reconstruct the data, in case I only have the extracted features. For example, I got 2 populations A and B:
Collect n1 features on population A -> yield dataset A -> train concrete autoencoder on it -> get n2 features of A
Collect n2 features on population B -> yield dataset B -> reconstruct n1 feature by using only the decoder -> get n2 feature of population B
Would you like to suggest/advise me how to to this? Thank you.
Hi, thanks for your interesting paper.
From my understanding, the concrete autoencoder is an autoencoder, with the latent space constrained by concrete distribution, and now work as a feature selector.
I want to know if it's possible to reconstruct the data, in case I only have the extracted features. For example, I got 2 populations A and B:
Would you like to suggest/advise me how to to this? Thank you.