Do you know how the weights and some ‘magic constants’ used in the FeatureLoss for the super resolution are determined?
feat_loss = FeatureLoss(vgg_m, blocks[2:5], [5,15,2])
in particular the [5,15,2] weights applied to the layers, why exactly those values?
The other one is the ‘5e3’ in the FeatureLoss definition and the squared ‘w’ ( w**2 ), which is applied to the ‘gram_matrix’ contribution to the loss:
self.feat_losses += [base_loss(gram_matrix(f_in), gram_matrix(f_out))*w**2 * 5e3
for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]
I think in this case those weights help fine tune the ‘style’ loss in this case, don’t they?
I would greatly appreciate if someone could shed some light on those value choices? do they come from a paper or have been determined empirically using a grid search?