説明可能AIのための効率的な顕著性マップ
Efficient Saliency Maps for Explainable AI
深層CNN向けに、各ネットワークスケールの情報を統合して高効率かつ高精度な顕著性マップを生成する手法を提案し、リソース制約のある環境での実用性を示した。
著者: T. Nathan Mundhenk, Barry Y. Chen, Gerald Friedland
分類: cs.CV, cs.LG, cs.PF, cs.RO
原文アブストラクト
We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. It is also quantitatively similar or better in accuracy. Our technique works by measuring information at the end of each network scale which is then combined into a single saliency map. We describe how saliency measures can be made more efficient by exploiting Saliency Map Order Equivalence. We visualize individual scale/layer contributions by using a Layer Ordered Visualization of Information. This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods. Using our method instead of Guided Backprop, coarse-resolution class activation methods such as Grad-CAM and Grad-CAM++ seem to yield demonstrably superior results without sacrificing speed. This will make fine-resolution saliency methods feasible on resource limited platforms such as robots, cell phones, low-cost industrial devices, astronomy and satellite imagery.