Interactive Analysis of CNN Robustness

In recent years, a wide spectrum of deep learning visualization methods has emerged. For a comprehensive overview of visual analytics (VA) for deep learning, we refer the reader to a survey by Hohman et al. A case study using a VA system to assess a model's performance to detect and classify traffic lights has shown that interactive VA systems can successfully guide experts to improve their training data While these examples all focus on the inspection of a single model, others support model comparisons. For example, using REMAP [cashman_ablate_2020], users can rapidly create model architectures through ablations (i.e., removing single layers of an existing model) and variations (i.e., creating new models through layer replacements) and compare the created models by their structure and performance. Interactive "playgrounds" require relatively little underlying deep learning knowledge and can be used for educational purposes.