ASU-CNN: An Efficient Deep Architecture for Image Classification and
  Feature Visualizations
Jamshaid Ul Rahman Faiza Makhdoom Dianchen Lu
Activation functions play a decisive role in determining the capacity of Deep
Neural Networks as they enable neural networks to capture inherent
nonlinearities present in data fed to them. The prior research on activation
functions primarily focused on the utility of monotonic or non-oscillatory
functions, until Growing Cosine Unit broke the taboo for a number of
applications. In this paper, a Convolutional Neural Network model named as
ASU-CNN is proposed which utilizes recently designed activation function ASU
across its layers. The effect of this non-monotonic and oscillatory function is
inspected through feature map visualizations from different convolutional
layers. The optimization of proposed network is offered by Adam with a
fine-tuned adjustment of learning rate. The network achieved promising results
on both training and testing data for the classification of CIFAR-10. The
experimental results affirm the computational feasibility and efficacy of the
proposed model for performing tasks related to the field of computer vision.