Analysing the Relations of Misclassified Inputs Between Models

A common thought in the machine learning community is that many of the misclassified images are "difficult" images (for example images where the details are too small to differentiate between two classes). We evaluate those misclassified images of various deep learning models and check if other models can correctly classify those images. We find that the misclassified images of each model are different. Moreover, despite that models have similar accuracy on ImageNet, one model can classify correctly more than 15\% of the misclassified images of another model. This means can encourage further research to use two or more architectures when performing a prediction.