通过使用图像翻译生成反事实的科学发现
Scientific Discovery by Generating Counterfactuals using Image Translation
摘要：
模型解释技术在理解模型性能的来源和使其决策透明方面起着至关重要的作用。在这里，我们调查，如果解释技术也可以作为一种机制，科学发现。我们做了三个贡献：首先，我们提出了一个框架，将预测从解释技术的发现机制。其次，我们展示了如何生成模型结合黑盒预测可以用来生成假设（没有人类先验），可以严格审查。第三，利用这些技术，我们研究了预测糖尿病黄斑水肿（DME）的视网膜图像的分类模型，最近的工作表明，在这些图像上训练的CNN可能正在学习图像中的新特征。我们证明了所提出的框架能够解释潜在的科学机制，从而弥合模型的性能和人类理解之间的差距。
英文摘要：
Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent. Here we investigate if explanation techniques can also be used as a mechanism for scientific discovery. We make three contributions: first, we propose a framework to convert predictions from explanation techniques to a mechanism of discovery. Second, we show how generative models in combination with black-box predictors can be used to generate hypotheses (without human priors) that can be critically examined. Third, with these techniques we study classification models for retinal images predicting Diabetic Macular Edema (DME), where recent work showed that a CNN trained on these images is likely learning novel features in the image. We demonstrate that the proposed framework is able to explain the underlying scientific mechanism, thus bridging the gap between the model's performance and human understanding.
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