try optimize resnet50 instead of latent code about stylegan-encoder HOT 10 CLOSED

Those are both w+, all truncation does is pull it more towards the center of the distribution.

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I'm not quite sure what you're proposing, but I'm welcome to proofs of concept or code! The resnet is mainly used to provide an estimate of the latents as a starting point for encoding.

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i mean after you have an estimate of the latents,connect the resnet and genarator , finetune resnet to get the best latents.
and i find after optimize the latent,generated-image looks blurry compared to the image generated by stylegan,does loss function lead to the effect?

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You could try that, but you'd just end up with a resnet model that's fine-tuned for a single image.

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@pbaylies line 125 in perceptual_model.py,why sess.run([self.features_weight.initializer, self.features_weight.initializer]) twice?

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For better or for worse, this was in @Puzer's original code as well.

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@pbaylies and i also want you help me make sure some details about stylegan:for images of1024 size,the w space is of[batchsize,18,512],after truncation ,get w+space of the same shape,how different of the two spaces?

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and，why do you use L1 loss on vgg features but use L2 loss on image pixels?what is your considerations when you design the five losses?

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Trial and error :)

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haha,you must try much losses.and images generated by stylegan always have defects or flaws,what reason do you think cause that?

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