Visual category learning by toddlers provides new principles for teaching rapid generalization

学习是对经验作出反应的适应性变化。未来的经历永远不会完全重复，世界经常会出现前所未有的新问题。因此，导致有效泛化的训练系统在人类和机器学习中都受到追捧。众所周知，对许多训练实例的丰富经验有助于推广，但这需要时间和大量的训练实例。有一些人类学习的案例，在这种情况下，适当的概括只需要一个或几个例子，或者“几次机会”的学习。人类婴儿开始学习成为视觉对象类别的几个初级学习者，例如，在看到拖拉机后，能够适当地概括类别“拖拉机”。这项研究将确定教幼儿成为视觉对象类别的少许学习者的学习经验，目的是确定导致快速学习和概括的训练集的一般原则。这些原则将在机器学习模型和儿童实验中进行测试。如何设计和组织培训材料以导致有效学习和快速推广的一般原则在教育、图像识别和机器学习中有有用的应用。类别学习的大多数模型和理论都集中在通过训练来学习区分类别，训练由许多不同类别的许多例子组成。尽管计算机视觉取得了显着的进步，但仍有一大类问题没有解决，因为它们需要很少的学习机会。尽管对教育实践进行了广泛的研究，但人们对如何最好地组织材料以便于推广和知识传授的了解有限。在成为视觉对象类别的极少镜头学习者的过程中，人类婴儿首先收集了对几个早期学习类别中的几个单独物体的广泛经验--他们自己的吸管杯子、家中的狗、自己的鞋子，然后他们逐渐成为其他视觉对象类别的快速极少镜头学习者。研究中检验的核心假设是，很少有机会的学习是对极少数类别的专业知识的概括。这项研究将利用婴儿佩戴的头部摄像头和头盔眼球追踪器，捕捉支持几个类别的专业知识发展的视觉体验。来自视觉科学和计算机视觉的算法将被用来分析这些视觉经验和专业知识的统计特性，然后将其推广到其他视觉类别。这些原理将在婴儿的行为实验中进行测试，并在使用深度卷积神经网络(CNN)的建模实验中进行测试和形式化。这些原则提供了一个解决问题的解决方案，从几个可能是一般性的例子到任何视觉学习问题。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Learning is adaptive change in response to experience. Future experiences never repeat exactly and the world often poses new never-before encountered problems. Therefore, training systems that lead to effective generalization are sought after in both human and machine learning. Extensive experience with many training examples is known to promote generalization but this requires time and a large set of training examples. There are cases of human learning in which appropriate generalization requires only one or a few examples, or 'few-shot' learning. The human infant begins learns to become a few-shot learner of visual object categories who is able, for example, to appropriately generalize the category 'tractor' after seeing a tractor. This research will determine the learning experiences that teach young children to be few-shot learners of visual object categories with the goal of determining the general principles of training sets that lead to rapid learning and generalization. These principles will be tested in machine learning models and in experiments with children. General principles of how to design and structure training materials to lead to effective learning and rapid generalization have useful applications in education, in image recognition, and in machine learning. Most models and theories of category learning concentrate on learning to discriminate categories with training consisting of many examples of many different categories. Despite remarkable advances in computer vision there is a large class of problems that remain unsolved because they require few-shot learning. Despite extensive research on educational practices, there is a limited understanding of how best to structure material for generalization and knowledge transfer. In becoming few-shot learners of visual object categories, human infants first collect extensive experience with a few individual objects within a few early-learned categories -- their own sippy cup, the family dog, their own shoes and then they progress to becoming rapid few-shot learners of other visual object categories. The core hypothesis tested in the research is that few-shot learning emerges as a generalization of expertise about a very few categories. The research will capture the visual experiences supporting the development of expertise for a few categories using head cameras and head-mounted eye trackers worn by infants. Algorithms from visual science and computer vision will be used to analyze the statistical properties of these visual experiences and of expertise that then generalizes to other visual categories. Those principles will be tested in behavioral experiments with infants and tested and formalized in modeling experiments using deep convolutional neural networks (CNNs). The principles offer a solution to the problem of rapid generalization from few examples that is potentially general to any visual learning problem.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.