论文标题
对象检测，识别，深度学习和普遍的泛化定律
Object Detection, Recognition, Deep Learning, and the Universal Law of Generalization
论文作者
论文摘要
对象检测和识别是物种成功的基本功能。由于对象的外观表现出很大的可变性，因此大脑必须将这些不同的刺激分组在相同的对象身份，即概括过程中。概括过程是否遵循一些一般原则，还是临时的“戏剧袋”？普遍的概括法提供了证据，表明概括遵循各种物种和任务的类似特性。在这里，我们检验了以下假设：概括的内部表示反映了对象检测和识别环境中的自然特性，而不是系统解决这些问题的细节。通过对“清晰”和“伪装”动物的图像进行深神经网络训练，我们发现，有了适当的类别原型选择，概括函数是单调的，类似于生物系统的概括函数。我们的发现支持研究的假设。
Object detection and recognition are fundamental functions underlying the success of species. Because the appearance of an object exhibits a large variability, the brain has to group these different stimuli under the same object identity, a process of generalization. Does the process of generalization follow some general principles or is it an ad-hoc "bag-of-tricks"? The Universal Law of Generalization provided evidence that generalization follows similar properties across a variety of species and tasks. Here we test the hypothesis that the internal representations underlying generalization reflect the natural properties of object detection and recognition in our environment rather than the specifics of the system solving these problems. By training a deep-neural-network with images of "clear" and "camouflaged" animals, we found that with a proper choice of category prototypes, the generalization functions are monotone decreasing, similar to the generalization functions of biological systems. Our findings support the hypothesis of the study.
