4 ms·
From the paper: > We used the Caffe implementation (Jia et al., 2014) of the VGG19 deep neural network (DNN) model (Simonyan & Zisserman, 2015; available from
by icc97 8y ago
From the paper:
> We used the Caffe implementation (Jia et al., 2014) of the VGG19 deep neural network (DNN) model (Simonyan & Zisserman, 2015; available from https://github.com/BVLC/caffe/wiki/Model https://github.com/BVLC/caffe/wiki/Model - Zoo) . All visual images were resized to 224 × 224 pixels to compute outputs by the VGG19 model . The VGG19 model consisted of a total of sixteen convolutional layers and three fully connected layers.
They used a VGG-19 network for the feature detection, so in my basic understanding the results of this could already be improved by switching to a ResNet.
- hardmaru 8y agoNot necessarily. While ResNets are optimized for the task of achieving the best image classification accuracy, it is no guarantee that the features extracted from it will transfer well to other tasks such as this one, especially when the task is to generate images. In fact, it has been shown that pre-trained VGG networks are a lot more useful than pre-trained ResNet for other tasks other than image classification, such as style transfer.
- icc97 8y agoVery interesting. For my curiosity I did some searching on VGG vs ResNet. * ResNets were better at feature extraction for image clustering [0] * One of the trade-offs of ResNets seems to be their relative complexity to VGG [1] * It surprises me that ResNets aren't significantly faster to train based on the large reduction of FLOPs (from the ResNet paper VGG-19 had 19.6 billion FLOPs vs ResNet-34 with 3.6 billion FLOPs) - I think people just train deeper ResNets e.g. ResNet-50 [0]: https://medium.com/@franky07724_57962/using-keras-pre-trained-models-for-feature-extraction-in-image-clustering-a142c6cdf5b1 https://medium.com/@franky07724_57962/using-keras-pre-traine... [1]: https://www.reddit.com/r/MachineLearning/comments/6e6mlf/d_is_vgg_common_in_newer_research_or_is_resnet/ https://www.reddit.com/r/MachineLearning/comments/6e6mlf/d_i...
- hardmaru 8y agoYou might enjoy this recent article on Distill: https://distill.pub/2018/differentiable-parameterizations/#section-styletransfer https://distill.pub/2018/differentiable-parameterizations/#s... They discuss VGG vs non-VGG architectures in the context of style transfer in Section 2, which was interesting to me.
- icc97 8y agoExcellent, 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...
- icc97 8y agoI now realise that that article comes back to your very own twitter post [0], thanks for your patience! [0]: https://twitter.com/hardmaru/status/954173051330904065 https://twitter.com/hardmaru/status/954173051330904065