VampPrior Generation

[Manslow]

I'm unsure of the role the pseudo inputs play in generation using a Vamp Propr. This is likey my lack of understanding of your paper because I find it hard to reconcile these implementation details with your paper (my lack of expertise in this area).

It seems that when you generate, you generate using a subset of the pseudo inputs as per:

means = self.means(self.idle_input)[0:N] z_sample_gen_mean, z_sample_gen_logvar = self.q_z(means) z_sample_rand = self.reparameterize(z_sample_gen_mean, z_sample_gen_logvar)

I understand how to generate from these pseudo inputs using reparameterization but how would I sample from the aggregated prior itself? After all, it is this prior that one would normally sample from in a VAE after all (though it wouldn't be aggregated in the vanilla VAE).

[FabricioArendTorres]

Isn't p(z2) simply a uniform mixture? So you could just uniformly sample one of the pseudo inputs u and then pass it through the encoder for getting a prior sample from p(z2).