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，通过视觉解释证明神经网络预测合理性的方法发展迅速。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重要性。伴随这一发展，评估方法被提出以衡量此类解释的"优劣"。一方面，部分方法依赖合成数据集，但这存在局限性：其在更真实场景中的适用性难以保证。另一方面，部分方法采用客观评估指标，但这些评估方法之间的性能相关性尚不明确。基于此，我们在ImageNet-1k验证集子集上开展了一项全面研究，评估多种常用解释方法及其对应的评估方法。我们通过合理性检验补充研究，以探究评估方法的可靠性及解释特性对评估方法的影响。研究结果表明，部分评估方法给出的评分缺乏一致性。此外，我们识别出解释的某些特性（如稀疏性）会对性能产生显著影响。