August 31, 2022
Github link
Abstract Neural networks are heavily used in image processing and classification. However, understanding the internal workings of neural networks has always been a tricky task. One way to look at how a convolutional neural network (CNN) works is by visualizing the feature maps which are obtained after passing a filter through an image. To better understand these feature maps and their purpose in CNN, we experimented with multiple ways of utilizing these in accelerating and enhancing the training of CNNs.