  In a hybrid neural network, the expensive convolutional layers are replaced
by a non-trainable fixed transform with a great reduction in parameters. In
previous works, good results were obtained by replacing the convolutions with
wavelets. However, wavelet based hybrid network inherited wavelet's lack of
vanishing moments along curves and its axis-bias. We propose to use Shearlets
with its robust support for important image features like edges, ridges and
blobs. The resulting network is called Complex Shearlets Network (CoShNet). It
was tested on Fashion-MNIST against ResNet-50 and Resnet-18, obtaining 92.2%
versus 90.7% and 91.8% respectively. The proposed network has 49.9k parameters
versus ResNet-18 with 11.18m and use 52 times fewer FLOPs. Finally, we trained
in under 20 epochs versus 200 epochs required by ResNet and do not need any
hyperparameter tuning nor regularization.
  Code: https://github.com/Ujjawal-K-Panchal/coshnet
