Excellent, digging down into that leads to this Reddit post [0] > One thing they noticed was that using features from a pretrained ImageNet VGG-16/19 CNN from

Excellent, digging down into that leads to this Reddit post [0] > One thing they noticed was that using features from a pretrained ImageNet VGG-16/19 CNN from 2014 (4 years ago), like the original Gatys paper did, worked much better than anything else; indeed, almost any set of 4-5 layers in VGG would provide great features for the style transfer optimization to target (as long as they were spread out and weren't exclusively bottom or top layers), while using more modern resnets (resnet-50) or GoogLeNet Inception v1 didn't work - it was hard to find sets of layers that would work at all and when they did, the quality of the style transfer was not as good. Interestingly, this appeared to be true of VGG CNNs trained on the MIT Places scene recognition database too, suggesting there's something architectural going on which is not database specific or peculiar to those two trained models. And their attempt at an upscaling CNN modeled on Johnson et al 2016's VGG-16 for CIFAR-100 worked well too. [0]: https://www.reddit.com/r/MachineLearning/comments/7rrrk3/d_eat_your_vggtables_or_why_does_neural_style/ https://www.reddit.com/r/MachineLearning/comments/7rrrk3/d_e...