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

学习是对经验的适应性改变。未来的经验永远不会完全重复，世界经常会出现新的从未遇到过的问题。因此，在人类和机器学习中都寻求有效泛化的训练系统。众所周知，大量的训练样例经验可以促进泛化，但这需要时间和大量的训练样例集。在人类学习的一些案例中，适当的泛化只需要一个或几个例子，或“少量”学习。人类婴儿开始学习成为视觉对象类别的少数学习者，例如，在看到拖拉机后能够适当地概括“拖拉机”类别。本研究将确定教幼儿成为视觉对象类别的少量学习者的学习经验，目的是确定导致快速学习和泛化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得到检验。如何设计和构建训练材料以导致有效学习和快速泛化的一般原则在教育，图像识别和机器学习中有有用的应用。大多数类别学习的模型和理论都集中在通过由许多不同类别的许多例子组成的训练来学习区分类别。尽管计算机视觉取得了显著的进步，但仍有大量的问题没有得到解决，因为它们需要很少的学习。尽管对教育实践进行了广泛的研究，但对如何最好地组织材料以进行泛化和知识转移的理解有限。在成为视觉对象类别的短时学习者的过程中，人类婴儿首先在几个早期学习的类别中——他们自己的吸管杯、家里的狗、他们自己的鞋子——收集了对几个单独对象的广泛经验，然后他们发展成为其他视觉对象类别的快速短时学习者。在这项研究中检验的核心假设是，短时学习是对少数类别的专业知识的概括。这项研究将捕捉视觉体验，通过婴儿佩戴的头戴式摄像头和头戴式眼动仪来支持一些类别的专业知识的发展。来自视觉科学和计算机视觉的算法将被用于分析这些视觉体验的统计特性，以及随后推广到其他视觉类别的专业知识。这些原则将在婴儿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。这些原则为从少数例子中快速泛化的问题提供了一个解决方案，这对任何视觉学习问题都可能是通用的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.