Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts. However, ad-hoc visual explanations of model decisions often reveal an alarming level of reliance on exploiting non-causal visual cues that strongly correlate with the target label in training data. As such, deep neural nets suffer compromised generalization to novel inputs collected from different sources, and the reverse engineering of their decision rules offers limited interpretability. To overcome these limitations, we present a novel contrastive learning strategy called {\it Proactive Pseudo-Intervention} (PPI) that leverages proactive interventions to guard against image features with no causal relevance. We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability. To demonstrate the utility of our proposals, we benchmark on both standard natural images and challenging medical image datasets. PPI-enhanced models consistently deliver superior performance relative to competing solutions, especially on out-of-domain predictions and data integration from heterogeneous sources. Further, our causally trained saliency maps are more succinct and meaningful relative to their non-causal counterparts.

翻译：深心神经网络非常擅长理解复杂的视觉信号,以与人类专家的同等或甚至优异的性能传递给人类专家。然而,对模型决定的快速直观解释往往揭示出对利用与培训数据目标标签密切相关的非因果视觉信号的依赖程度令人震惊。因此,深心神经网对从不同来源收集的新投入的概括性影响很大,其决定规则的反向工程提供了有限的解释性。为了克服这些限制,我们提出了一个新的对比性学习战略,即“全动性普塞多-Interprotection”(PPI),利用积极主动的干预措施来防范无因果关系的图像特征。我们还设计了一个新的、有因果关系的突出特征绘图模块,以确定要干预的关键图像像素,并展示它非常有利于模型的可解释性。为了展示我们提案的实用性,我们以标准自然图像和具有挑战性的医疗图像数据集为基准。PPI强化模型不断提供优于竞争性解决方案的绩效,特别是在外部预测和来自不同来源的数据整合方面。此外,我们经过因果关系分析的突出的地图相对于非有意义的对应方。