Wide Residual Networks (Zagoruyko & Komodakis) have proven well in image classification. They prioritize width instead of the traditional thin and deep structure of classical resnets, thus allowing a faster training due to GPU or TPU parallelization.
That would increase the speed of research since less time would be consumed to train the Networks. Those models are already implemented in Keras @
https://github.com/EricAlcaide/keras-wrn and the code ca just be copy-pasted and added to this package.
Wide Residual Networks (Zagoruyko & Komodakis) have proven well in image classification. They prioritize width instead of the traditional thin and deep structure of classical resnets, thus allowing a faster training due to GPU or TPU parallelization.
That would increase the speed of research since less time would be consumed to train the Networks. Those models are already implemented in Keras @ https://github.com/EricAlcaide/keras-wrn and the code ca just be copy-pasted and added to this package.