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We assess the tendency of state-of-the-art object recognition models todepend on signals from image backgrounds. We create a toolkit for disentanglingforeground and background signal on ImageNet images, and find that (a) modelscan achieve non-trivial accuracy by relying on the background alone, (b) modelsoften misclassify images even in the presence of correctly classifiedforegrounds--up to 87.5% of the time with adversarially chosen backgrounds, and(c) more accurate models tend to depend on backgrounds less. Our analysis ofbackgrounds brings us closer to understanding which correlations machinelearning models use, and how they determine models' out of distributionperformance.

Noise or Signal: The Role of Image Backgrounds in Object Recognition