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In factor vae, Disentangling by Factorising, there are two losses that are minimized. One is the VAE loss (eq. 2 in the paper) that includes (1) reconstruction loss, (2) KL divergence and (3) Total correlation loss, which is obtained by using a discriminator network which itself is trained via the minimization of the Discriminator loss. Should the discriminator network weights be frozen while performing backpropagation to minimize the VAE loss?

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  • $\begingroup$ I had misread your question, I thought you wanted to know if the discriminator was pretrained $\endgroup$
    – Firebug
    Jul 1, 2023 at 8:26

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Yes, when training the VAE, you should freeze the weights of the discriminator and only update the VAE parameters ($\theta$).

Algorithm 2 provides a nice summary of how to jointly train the two models (take notice that the parameters ($\theta, \psi$) are updated separately).

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    $\begingroup$ Yes, thanks! I noticed that in algorithm 2 as well and had included in a comment, but somehow the comment got deleted by someone. Thought I'd confirm my understanding here after seeing a couple of github implementations where the freezing was not done. $\endgroup$
    – S R
    Jul 6, 2023 at 19:23

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