The generator we use in our paper is a form of restriction of 'direct' training data. You can think of it as a weird encoding for images.
PS: I'm the author of the GTN paper. Feel free to ask any questions
PS: I'm the author of the GTN paper. Feel free to ask any questions
I tried implementing it, and the samples generated by the Teacher seem to suffer from mode collapse (as if the generator is ignoring the random vector z but not the label condition). Do you recall having that issue at some point?
I have to say I'm using a simpler generator than the one in the paper, and I'm not changing the learner architechture at each batch, only its weights.
Thanks!
Changing the learner architecture is not that important actually so that's probably not your problem.
Hi, thanks for the invitation!
How can you be sure that the synthetic data you generate does not bias the architecture search away from the optimal solution for real data in a way similar to how early truncation of learning biases architecture search towards quick learners, and possibly away from peak performers?