Recent years have shown an increased development of methods for justifying the predictions of neural networks through visual explanations. These explanations usually take the form of heatmaps which assign a saliency (or relevance) value to each pixel of the input image that expresses how relevant the pixel is for the prediction of a label. Complementing this development, evaluation methods have been proposed to assess the "goodness" of such explanations. On the one hand, some of these methods rely on synthetic datasets. However, this introduces the weakness of having limited guarantees regarding their applicability on more realistic settings. On the other hand, some methods rely on metrics for objective evaluation. However the level to which some of these evaluation methods perform with respect to each other is uncertain. Taking this into account, we conduct a comprehensive study on a subset of the ImageNet-1k validation set where we evaluate a number of different commonly-used explanation methods following a set of evaluation methods. We complement our study with sanity checks on the studied evaluation methods as a means to investigate their reliability and the impact of characteristics of the explanations on the evaluation methods. Results of our study suggest that there is a lack of coherency on the grading provided by some of the considered evaluation methods. Moreover, we have identified some characteristics of the explanations, e.g. sparsity, which can have a significant effect on the performance.

翻译：近年来，通过视觉解释论证神经网络预测的方法得到了日益发展。这类解释通常采用热力图形式，为输入图像的每个像素分配显著性（或相关性）值，以表明该像素对标签预测的重要程度。与此发展相呼应，研究者提出了评估方法来判断此类解释的"优良性"。一方面，部分方法依赖于合成数据集，但这存在局限性——其在更现实场景中的适用性缺乏充分保证。另一方面，部分方法采用基于指标的客观评估，但不同评估方法之间的相对表现水平尚不确定。基于此，我们在ImageNet-1k验证集子集上开展了全面研究，采用一组评估方法对多种常用解释方法进行系统评估。我们通过合理性检验对评估方法进行辅助分析，以探究其可靠性及解释特征对评估方法的影响。研究结果表明，部分评估方法提供的评分缺乏一致性。此外，我们识别了解释的某些特征（如稀疏性）可能对评估结果产生显著影响。