Common deep neural networks (DNNs) for image classification have been shown
to rely on shortcut opportunities (SO) in the form of predictive and
easy-to-represent visual factors. This is known as shortcut learning and leads
to impaired generalization. In this work, we show that common DNNs also suffer
from shortcut learning when predicting only basic visual object factors of
variation (FoV) such as shape, color, or texture. We argue that besides
shortcut opportunities, generalization opportunities (GO) are also an inherent
part of real-world vision data and arise from partial independence between
predicted classes and FoVs. We also argue that it is necessary for DNNs to
exploit GO to overcome shortcut learning. Our core contribution is to introduce
the Diagnostic Vision Benchmark suite DiagViB-6, which includes datasets and
metrics to study a network's shortcut vulnerability and generalization
capability for six independent FoV. In particular, DiagViB-6 allows controlling
the type and degree of SO and GO in a dataset. We benchmark a wide range of
popular vision architectures and show that they can exploit GO only to a
limited extent.

本研究发现常见的深度神经网络在预测基本的视觉目标因素（例如形状、颜色或纹理）时也会受到捷径学习的影响，且需要利用视觉数据中的独立性来克服此问题，从而引入了诊断视觉基准套件 DiagViB-6，并基准测试了多种流行的视觉体系结构并表明它们只能在有限程度上利用视觉数据中的独立性。