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，通过视觉解释来论证神经网络预测的方法得到了日益发展。这些解释通常以热力图形式呈现，为输入图像的每个像素分配一个显著性（或相关性）值，以表示该像素对标签预测的相关程度。为配合这一发展，评估方法被提出以衡量此类解释的“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，即难以保证其在更现实场景下的适用性。另一方面，部分方法采用基于指标的客观评估，但其中一些评估方法相互之间的表现程度尚不确定。据此，我们在ImageNet-1k验证集的一个子集上开展了全面研究，按照一组评估方法评估了多种常用解释方法。我们通过所研究评估方法的合理性检验来补充研究，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，部分待考评估方法提供的评分缺乏一致性。此外，我们识别了某些解释特征（例如稀疏性）可能对性能产生显著影响。