论文标题
噪声或信号：图像背景在对象识别中的作用
Noise or Signal: The Role of Image Backgrounds in Object Recognition
论文作者
论文摘要
我们评估了最先进的对象识别模型的趋势，以取决于图像背景的信号。我们创建了一个工具包，用于在Imagenet图像上解开前景和背景信号，并发现（a）模型可以单独依靠背景来实现非平凡的准确性，（b）模型即使在正确分类的前景存在的情况下，模型也经常错误地分类图像，即与对手选择的背景的87.5％，以及（c）更准确的型号，并且更依赖背景。我们对背景的分析使我们更加了解机器学习模型使用哪些相关性以及如何确定模型的分配性能。
We assess the tendency of state-of-the-art object recognition models to depend on signals from image backgrounds. We create a toolkit for disentangling foreground and background signal on ImageNet images, and find that (a) models can achieve non-trivial accuracy by relying on the background alone, (b) models often misclassify images even in the presence of correctly classified foregrounds--up to 87.5% of the time with adversarially chosen backgrounds, and (c) more accurate models tend to depend on backgrounds less. Our analysis of backgrounds brings us closer to understanding which correlations machine learning models use, and how they determine models' out of distribution performance.
