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

学习是对经验的适应性变化。 未来的经验永远不会完全重复，世界往往提出新的从未遇到过的问题。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，对许多训练示例的丰富经验可以促进泛化，但这需要时间和大量的训练示例。 在人类学习的某些情况下，适当的概括只需要一个或几个例子，或“几次”学习。人类婴儿开始学习成为视觉对象类别的几次学习者，例如，在看到拖拉机后能够适当地概括“拖拉机”类别。这项研究将确定的学习经验，教幼儿是少数拍摄学习者的视觉对象类别的目标，确定的一般原则，导致快速学习和泛化的训练集。 这些原则将在机器学习模型和儿童实验中进行测试。如何设计和构造训练材料以实现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具有有用的应用。 大多数类别学习的模型和理论都集中在学习区分类别，训练由许多不同类别的许多示例组成。尽管计算机视觉取得了显着的进步，但仍有一大类问题尚未解决，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，但对如何最好地构建材料以进行概括和知识转移的理解有限。在成为视觉对象类别的少数学习者的过程中，人类婴儿首先在几个早期学习的类别中收集了大量的个别对象经验-他们自己的吸管杯，家里的狗，他们自己的鞋子然后他们逐渐成为其他视觉对象类别的快速少数学习者。在研究中测试的核心假设是，少量学习是对少数几个类别的专业知识的概括。该研究将捕捉视觉体验，支持使用婴儿佩戴的头部摄像机和头戴式眼动仪开发少数类别的专业知识。来自视觉科学和计算机视觉的算法将用于分析这些视觉体验的统计特性，并将其推广到其他视觉类别。 这些原则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方案，从几个例子，是潜在的任何视觉学习问题的一般快速概括的问题。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.