Do you know how the weights and some ‘magic constants’ used in the FeatureLoss for the super resolution are determined?

Part 1 (2019) Advanced (Part 1 v3)
#1

Hi there,

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?

Thanks!

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#2

The layer weights for style losses are human input based on fast experiment by running the training few times and print all the losses. Rule of thumb: try to make these losses magnitude are close.

Source: https://mc.ai/neural-style-transfer-with-deep-vgg-model/

If you take a look at the function definition for FeatureLoss, in the forward pass definition you will find the line

self.feat_losses += [base_loss(f_in, f_out)*w for f_in, f_out, w in zip(in_feat, out_feat, self.wgts)]

Where the magic constants for the weight layers - self.wgts -, define the importance of each layer loss. So, if you are doing a style transfer, the weight would have a big impact on the resulting images.

PS: I’m not an expert.

1 Like
#3

An old thread, but in case someone drops by, you reference pointed to this article, which shades some light:

In essence, one has to experimentally set those weights. Thanks!