Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to the suggestion that DNNs may also be good models of human visual perception. In this article, we review evidence regarding current DNNs as adequate behavioral models of human core object recognition. To this end, we argue that it is important to distinguish between statistical tools and computational models and to understand model quality as a multidimensional concept in which clarity about modeling goals is key. Reviewing a large number of psychophysical and computational explorations of core object recognition performance in humans and DNNs, we argue that DNNs are highly valuable scientific tools but that, as of today, DNNs should only be regarded as promising—but not yet adequate—computational models of human core object recognition behavior. On the way, we dispel several myths surrounding DNNs in vision science.

中文翻译：

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深度神经网络是否足以作为人类视觉感知的行为模型？

深度神经网络 (DNN) 是机器学习算法，由于在对象分类和分割等任务中取得了显着的成功，彻底改变了计算机视觉。 DNN 作为计算机视觉算法的成功表明，DNN 也可能是人类视觉感知的良好模型。在本文中，我们回顾了有关当前 DNN 作为人类核心对象识别的适当行为模型的证据。为此，我们认为区分统计工具和计算模型并将模型质量理解为一个多维概念非常重要，其中建模目标的清晰度是关键。回顾人类和 DNN 核心对象识别性能的大量心理物理学和计算探索，我们认为 DNN 是非常有价值的科学工具，但截至目前，DNN 只能被视为有前途（但还不够充分）的计算模型人类核心物体识别行为。在此过程中，我们消除了视觉科学中有关 DNN 的几个神话。