We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system.

通过对三种著名的深度卷积神经网络在十二种不同的图像失真下的实验对比，研究表明，与这三种人工智能算法相比，人类视觉系统在几乎所有的测试图像处理中都更加鲁棒，其误差模式渐行渐远。本文还证明了，利用失真图像直接训练的深度学习神经网络在恰当的测试条件下，表现优于人类。然而，当被分别用于测试不同类型的失真图像时，它们的泛化能力极差，无法适应噪声分布的变化，这成为深度学习视觉系统所面临的关键挑战，可用一种终身机器学习的方法进行系统化解决。

人类与深度神经网络的泛化能力