Demystifying Convolutional Neural Networks Using Class Activation Maps.

Machine Learning is gaining exponential momentum every day and its applications are increasing in every domain whether it be a trivial stock price prediction in finance domain or a complex task like detection and segmentation of Objects in Computer Vision Domain. No domain is left untouched by the AI revolution and in some domains, the Machine Learning Algorithms are even surpassing human-level performance. For example, ImageNet challenge was organised every year for various Computer Vision tasks like Image Classification, Object Detection, Image Localization etc and each year the error rates of the best-performing algorithms kept on decreasing and in 2017, 29 of 38 competing teams had greater than 95% accuracy. Human top-5 classification error rate on the large scale ImageNet dataset has been reported to be 5.1% whereas the state of the art CNN achieves an accuracy of about 3.57%. With the increasing performance of Machine Learning Systems, the interpretability of the systems are gradually decreasing. This trend is seen more in Deep Learning Algorithms comprising of millions of parameters and hundreds of layers making it extremely difficult to interpret them as compared to basic machine learning algorithms like Linear Regression, K Nearest Neighbours, Decision Tree etc.