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Three major research directions in explainable deep learning: understanding, debugging, and refinement/steering
Model understanding
aims to explain the rationale behind model predictions and the inner workings of deep learning models, and it attempts to make these complex models at least partly understanding
Perturbation experiments (CVPR2014): Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model archite