Feature Extractor Based on Class Specific Hidden Neuron Activations for Image Classification

Extracting good discriminative features plays a significant role in the predictive accuracy of any machine learning model. Engineering good features from raw data is a non-trivial and often a time-consuming task. Models like Convolutional Neural Networks (CNNs) have been very popular in the image classification tasks. This is due to CNN's excellent predictive capabilities and ability to automatically learn good features from raw data. One inherent draw back for CNNs and other deep learning models is that they are black-box models. The predictions made by these models cannot be explained based on features learned by them. In this paper, we put forth a novel feature extractor in which the features are automatically extracted from hidden neurons and convolutional neurons. The predictions made by the model are explained using the visualizations of the activation functions of the class specific neurons in hidden layers. Thus, the model put forth in this paper has excellent predictive capabilities and the predictions can be explained based on activation functions of class specific neurons.