IMACS：图像模型属性比较总结
IMACS: Image Model Attribution Comparison Summaries
摘要：
开发合适的深度神经网络（DNN）通常需要大量迭代，其中需要评估和比较不同的模型版本。虽然准确性等指标是简洁地描述模型在数据集上的性能或直接比较模型版本的强大手段，但从业者通常希望更深入地了解影响模型预测的因素。基于梯度的方法和局部近似等可解释性技术可以用于详细检查小的输入集合，但很难确定小集合的结果是否适用于整个数据集。我们介绍了IMACS，这是一种将基于梯度的模型属性与聚合和可视化技术相结合的方法，用于总结两个DNN图像模型之间的属性差异。更具体地说，IMACS从评估数据集中提取突出的输入特征，基于相似性对它们进行聚类，然后可视化相似输入特征的模型属性差异。在这项工作中，我们介绍了一个框架，用于聚合，总结和比较数据集上两个模型的属性信息;呈现突出2个图像分类模型之间差异的可视化;并展示了我们的技术如何发现在卫星图像上训练的两个模型之间的域转移引起的行为差异。
英文摘要：
Developing a suitable Deep Neural Network (DNN) often requires significant iteration, where different model versions are evaluated and compared. While metrics such as accuracy are a powerful means to succinctly describe a model's performance across a dataset or to directly compare model versions, practitioners often wish to gain a deeper understanding of the factors that influence a model's predictions. Interpretability techniques such as gradient-based methods and local approximations can be used to examine small sets of inputs in fine detail, but it can be hard to determine if results from small sets generalize across a dataset. We introduce IMACS, a method that combines gradient-based model attributions with aggregation and visualization techniques to summarize differences in attributions between two DNN image models. More specifically, IMACS extracts salient input features from an evaluation dataset, clusters them based on similarity, then visualizes differences in model attributions for similar input features. In this work, we introduce a framework for aggregating, summarizing, and comparing the attribution information for two models across a dataset; present visualizations that highlight differences between 2 image classification models; and show how our technique can uncover behavioral differences caused by domain shift between two models trained on satellite images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
