An effective way to obtain different perspectives on any given topic is by conducting a debate, where participants argue for and against the topic. Here, we propose a novel debate framework for understanding and explaining a continuous image classifier's reasoning for making a particular prediction by modeling it as a multiplayer sequential zero-sum debate game. The contrastive nature of our framework encourages players to learn to put forward diverse arguments during the debates, picking up the reasoning trails missed by their opponents and highlighting any uncertainties in the classifier. Specifically, in our proposed setup, players propose arguments, drawn from the classifier's discretized latent knowledge, to support or oppose the classifier's decision. The resulting Visual Debates collect supporting and opposing features from the discretized latent space of the classifier, serving as explanations for the internal reasoning of the classifier towards its predictions. We demonstrate and evaluate (a practical realization of) our Visual Debates on the geometric SHAPE and MNIST datasets and on the high-resolution animal faces (AFHQ) dataset, along standard evaluation metrics for explanations (i.e. faithfulness and completeness) and novel, bespoke metrics for visual debates as explanations (consensus and split ratio).

翻译：理解并解释连续图像分类器做出特定预测的推理过程，一种有效途径是组织辩论：参与者就某一议题展开支持与反对的论证。本文提出一种新颖的辩论框架，通过将连续图像分类器的推理过程建模为多人顺序零和辩论博弈，来理解并解释其做出特定预测的推理过程。该框架的对比性本质促使玩家在辩论中学习提出多样化的论点，捕捉对手遗漏的推理线索，并凸显分类器中的任何不确定性。具体而言，在本文提出的设置中，玩家从分类器的离散化潜在知识中提取论点，以支持或反对分类器的决策。由此产生的视觉辩论从分类器的离散化潜在空间中收集支持性与反对性特征，这些特征作为分类器对其预测的内部推理的解释。我们在几何形状SHAPE数据集、MNIST数据集以及高分辨率动物面部（AFHQ）数据集上，结合解释的标准评估指标（即忠实度与完整性）以及专门为作为解释的视觉辩论设计的新型定制指标（共识率与分裂比），对视觉辩论（的实际实现）进行了演示与评估。