When it comes to deploying deep vision models, the behavior of these systems must be explicable to ensure confidence in their reliability and fairness. A common approach to evaluate deep learning models is to build a labeled test set with attributes of interest and assess how well it performs. However, creating a balanced test set (i.e., one that is uniformly sampled over all the important traits) is often time-consuming, expensive, and prone to mistakes. The question we try to address is: can we evaluate the sensitivity of deep learning models to arbitrary visual attributes without an annotated test set? This paper argues the case that Zero-shot Model Diagnosis (ZOOM) is possible without the need for a test set nor labeling. To avoid the need for test sets, our system relies on a generative model and CLIP. The key idea is enabling the user to select a set of prompts (relevant to the problem) and our system will automatically search for semantic counterfactual images (i.e., synthesized images that flip the prediction in the case of a binary classifier) using the generative model. We evaluate several visual tasks (classification, key-point detection, and segmentation) in multiple visual domains to demonstrate the viability of our methodology. Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.
翻译:当部署深度视觉模型时,这些系统的行为必须是可解释的,以确保对其可靠性和公正性的信心。评估深度学习模型的常见方法是构建一个带有感兴趣属性的标记测试集,并评估其表现如何。然而,创建均衡的测试集(例如在重要特征上平衡采样的测试集)通常耗时费用高且容易出错。我们试图解决的问题是:能否在没有标记测试集的情况下评估深度学习模型对任意视觉属性的敏感性?本文认为,可以实现零样本模型诊断(ZOOM),而不需要测试集或标注。为了避免需要测试集,我们的系统依赖于生成模型和CLIP。关键思想是使用户选择一组提示(与问题相关),我们的系统将利用生成模型自动搜索语义反事实图像(即,在二元分类器的情况下翻转预测的合成图像)。我们在多个视觉领域中评估了多个视觉任务(分类、关键点检测和分割),以证明我们方法的可行性。广泛的实验证明,我们的方法能够在不需要测试集的情况下产生反事实图像,并为模型诊断提供敏感性分析。