For now, we show that if StarGAN is trained on images augmented by adversarial attack it does become more resistant but not completely resistant to attacks.
35 karma · joined October 15, 2019
Reviewers asked for a comparison with this specific random search. You can go look at the reviews on OpenReview.
I believe in this specific case, there was a local optimum that random search (with the parameters we selected, which we actually tuned as well) was not able to escape.
In most cases I think you would find random search doing better than random parameters (unless we have the paradoxical situation you described), so your intuition is correct in the general case. But in this case there is no typo on the table!
Does this answer your question?
Random search is a good alternative and it did produce good results for the car counting experiment. We do not claim that RL is the best way to solve the problem at any point in our work. We just observe that random search did not perform well in the segmentation experiment while RL did perform well in all of our experiments. We think the RL formulation is a good one since it is flexible (can be easily adapted to neural network policies with thousands of weights for example).
Hope this helped!