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:近些年来,通过直观解释对神经网络作出预测的方法有了越来越多的发展,这些解释通常采取热图的形式,给每个输入图像像素赋予一个显著(或相关性)价值,表明象素对标签预测的关联性。为了补充这一发展,提出了评估方法,以评估这种解释的“良好性”。一方面,其中一些方法依靠合成数据集,但这种方法存在缺陷,即对其适用于更现实环境的保证有限。另一方面,有些方法依靠指标进行客观评价。然而,这些评价方法中某些相互之间执行的程度尚不确定。考虑到这一点,我们对图像Net-1k验证集的一个子进行了全面研究,我们根据一套评价方法对一些不同的常用解释方法进行评估。我们用这些方法来补充我们的研究,对所研究的评价方法的可靠性和解释特点对评价方法的影响进行精准性检查,以调查其可靠性和对评估方法的影响。我们研究的结果表明,有些评估方法缺乏一致性。我们研究了某种显著的特征。我们研究了这些特征。</s>