Saliency methods have become standard in the explanation toolkit of deep neural networks. Recent developments specific to image classifiers have investigated region-based explanations with either new methods or by adapting well-established ones using ad-hoc superpixel algorithms. In this paper, we aim to avoid relying on these segmenters by extracting a segmentation from the activations of a deep neural network image classifier without fine-tuning the network. Our so-called Neuro-Activated Superpixels (NAS) can isolate the regions of interest in the input relevant to the model's prediction, which boosts high-threshold weakly supervised object localization performance. This property enables the semi-supervised semantic evaluation of saliency methods. The aggregation of NAS with existing saliency methods eases their interpretation and reveals the inconsistencies of the widely used area under the relevance curve metric.

翻译：显著性方法已成为深度神经网络解释工具包中的标准技术。针对图像分类器的近期研究通过开发新方法或采用临时超像素算法调整成熟方法，探索了基于区域的解释。本文旨在避免依赖这些分割器，通过从深度神经网络图像分类器的激活中提取分割结果，而无需对网络进行微调。我们提出的神经激活超像素（NAS）能够分离输入中与模型预测相关的感兴趣区域，从而提升高阈值弱监督目标定位性能。这一特性支持对显著性方法进行半监督语义评估。将NAS与现有显著性方法相结合，可简化其解释过程，并揭示广泛使用的相关性曲线下面积度量指标的不一致性。