论文标题
对机器中形状与纹理偏置的开发启发的检查
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines
论文作者
论文摘要
在发展的早期，儿童学会将新型类别标签扩展到具有相同形状的物体，这种现象称为形状偏差。受这些发现的启发，Geirhos等人。 （2019年）检查了深层神经网络是否通过构造具有冲突形状和纹理提示的图像来表现出形状或纹理偏见。他们发现，卷积神经网络强烈希望根据纹理而不是形状对熟悉的对象进行分类，这表明质地偏差。但是，在这项研究中测试网络与通常如何测试的孩子之间存在许多差异。在这项工作中，我们通过适应Geirhos等人的刺激和程序来重新检查神经网络的诱导偏见。 （2019年）更紧密地遵循发展范式并在广泛的预训练的神经网络上进行测试。在三个实验中，我们发现，在更紧密地复制发展程序的条件下进行测试时，深层神经网络偏爱形状而不是纹理。
Early in development, children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al. (2019) examined whether deep neural networks show a shape or texture bias by constructing images with conflicting shape and texture cues. They found that convolutional neural networks strongly preferred to classify familiar objects based on texture as opposed to shape, suggesting a texture bias. However, there are a number of differences between how the networks were tested in this study versus how children are typically tested. In this work, we re-examine the inductive biases of neural networks by adapting the stimuli and procedure from Geirhos et al. (2019) to more closely follow the developmental paradigm and test on a wide range of pre-trained neural networks. Across three experiments, we find that deep neural networks exhibit a preference for shape rather than texture when tested under conditions that more closely replicate the developmental procedure.
