It is known that humans display "shape bias" when classifying new items,
i.e., they prefer to categorize objects based on their shape rather than color.
Convolutional Neural Networks (CNNs) are also designed to take into account the
spatial structure of image data. In fact, experiments on image datasets,
consisting of triples of a probe image, a shape-match and a color-match, have
shown that one-shot learning models display shape bias as well.
In this paper, we examine the shape bias property of CNNs. In order to
conduct large scale experiments, we propose using the model accuracy on images
with reversed brightness as a metric to evaluate the shape bias property. Such
images, called negative images, contain objects that have the same shape as
original images, but with different colors. Through extensive systematic
experiments, we investigate the role of different factors, such as training
data, model architecture, initialization and regularization techniques, on the
shape bias property of CNNs. We show that it is possible to design different
CNNs that achieve similar accuracy on original images, but perform
significantly different on negative images, suggesting that CNNs do not
intrinsically display shape bias. We then show that CNNs are able to learn and
generalize the structures, when the model is properly initialized or data is
properly augmented, and if batch normalization is used.

本文探讨了卷积神经网络的形状偏差属性，通过使用将亮度反转的负图像等度量方法评估 CNNs 的形状偏差属性，进行大规模实验并研究了不同因素，如训练数据、模型架构、初始化和规则化技术，对 CNNs 形状偏差属性的作用。结果表明，CNNs 不会固有地表现出形状偏差，但是适当初始化模型或适当增强数据和使用批归一化技术，可以使 CNNs 学习和推广结构。