论文标题
到处都是虚假特征 - 图像网中大规模检测有害伪造特征
Spurious Features Everywhere -- Large-Scale Detection of Harmful Spurious Features in ImageNet
论文作者
论文摘要
仅深度学习分类器的基准性能并不是部署模型性能的可靠预测指标。特别是，如果图像分类器在培训数据中掌握了虚假功能，则其预测可能会出乎意料。在本文中，我们开发了一个框架，使我们能够系统地识别ImageNet之类的大数据集中的虚假特征。它基于我们的神经PCA组件及其可视化。以前关于伪造功能的工作通常在玩具设置中运行，或者需要昂贵的像素注释。相比之下，我们通过表明单独的班级有害的伪造特征的存在足以触发该类别的预测，从而与ImageNet合作并验证结果。我们介绍了新颖的数据集“伪造成像网”，该数据集允许测量任何成像网分类器对有害伪造特征的依赖。此外，我们将SPUFIX作为一种简单的缓解方法介绍，以减少任何成像网分类器对先前确定的有害伪造特征的依赖性，而无需其他标签或重新培训模型。我们在https://github.com/yanneu/spurious_imagenet上提供代码和数据。
Benchmark performance of deep learning classifiers alone is not a reliable predictor for the performance of a deployed model. In particular, if the image classifier has picked up spurious features in the training data, its predictions can fail in unexpected ways. In this paper, we develop a framework that allows us to systematically identify spurious features in large datasets like ImageNet. It is based on our neural PCA components and their visualization. Previous work on spurious features often operates in toy settings or requires costly pixel-wise annotations. In contrast, we work with ImageNet and validate our results by showing that presence of the harmful spurious feature of a class alone is sufficient to trigger the prediction of that class. We introduce the novel dataset "Spurious ImageNet" which allows to measure the reliance of any ImageNet classifier on harmful spurious features. Moreover, we introduce SpuFix as a simple mitigation method to reduce the dependence of any ImageNet classifier on previously identified harmful spurious features without requiring additional labels or retraining of the model. We provide code and data at https://github.com/YanNeu/spurious_imagenet .
