深度视觉解释导论
An Introduction to Deep Visual Explanation
摘要：
深度学习对复杂的监督学习问题的实际影响是巨大的，以至于几乎每一个人工智能问题，或者至少其中的一部分，都被重新定义为深度学习问题。应用程序的吸引力是显著的，但这种吸引力越来越受到一些人所谓的可解释性挑战的挑战，或者更一般地说，更传统的可调试性挑战：如果深度学习过程的结果产生了意想不到的结果（例如，低于分类器的预期性能），那么几乎没有可用的理论或工具来帮助调查这种意外行为的潜在原因，特别是当这种行为可能影响人们的生活时。我们描述了一个初步的框架，以帮助解决这个问题，我们称之为“深度视觉解释”（DVE）。“深”，因为它是我们想了解的深度神经网络模型的发展和性能。“视觉”，因为我们相信，对一个复杂的多维模型的最快速的洞察力是由适当的可视化技术提供的，而“解释”，因为在从插入打印语句到解释性假设的溯因推理的仪器化过程中，我们认为，理解深度学习的关键在于识别和揭示关于学习深度的性能行为的假设。模型在我们的初步框架的阐述中，我们使用相对简单的图像分类示例和深度模型构建场景的初始配置的各种选择。通过仔细但不复杂的仪器，我们使用可视化揭示了深层模型的分类结果，并显示了可解释性的一个潜在应用的初步结果。
英文摘要：
The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem. The applications appeal is significant, but this appeal is increasingly challenged by what some call the challenge of explainability, or more generally the more traditional challenge of debuggability: if the outcomes of a deep learning process produce unexpected results (e.g., less than expected performance of a classifier), then there is little available in the way of theories or tools to help investigate the potential causes of such unexpected behavior, especially when this behavior could impact people's lives. We describe a preliminary framework to help address this issue, which we call "deep visual explanation" (DVE). "Deep," because it is the development and performance of deep neural network models that we want to understand. "Visual," because we believe that the most rapid insight into a complex multi-dimensional model is provided by appropriate visualization techniques, and "Explanation," because in the spectrum from instrumentation by inserting print statements to the abductive inference of explanatory hypotheses, we believe that the key to understanding deep learning relies on the identification and exposure of hypotheses about the performance behavior of a learned deep model. In the exposition of our preliminary framework, we use relatively straightforward image classification examples and a variety of choices on initial configuration of a deep model building scenario. By careful but not complicated instrumentation, we expose classification outcomes of deep models using visualization, and also show initial results for one potential application of interpretability.
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