Counterfactual explanations are an emerging tool to enhance interpretability of deep learning models. Given a sample, these methods seek to find and display to the user similar samples across the decision boundary. In this paper, we propose a generative adversarial counterfactual approach for satellite image time series in a multi-class setting for the land cover classification task. One of the distinctive features of the proposed approach is the lack of prior assumption on the targeted class for a given counterfactual explanation. This inherent flexibility allows for the discovery of interesting information on the relationship between land cover classes. The other feature consists of encouraging the counterfactual to differ from the original sample only in a small and compact temporal segment. These time-contiguous perturbations allow for a much sparser and, thus, interpretable solution. Furthermore, plausibility/realism of the generated counterfactual explanations is enforced via the proposed adversarial learning strategy.
翻译:反事实解释是提高深层学习模型可解释性的一种新兴工具。根据一个样本,这些方法寻求在决定边界范围内寻找并向用户展示类似的样本。在本文中,我们提议在土地覆盖分类任务的多级设置中,对卫星图像的多级图像时间序列采用基因对抗式反事实方法。拟议方法的特征之一是对特定反事实解释的目标类别缺乏事先假设。这种固有的灵活性使得能够发现关于土地覆盖类别之间关系的有趣信息。另一个特征是鼓励反事实只在小型和紧凑的时段中与原始样本不同。这些时间相联的扰动允许非常稀疏,从而可以解释解决办法。此外,生成的反事实解释的可信度/现实性通过拟议的对抗性学习战略得以实施。