Smith Linda B, Slone Lauren K
Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States.
Front Psychol. 2017 Dec 5;8:2124. doi: 10.3389/fpsyg.2017.02124. eCollection 2017.
Visual learning depends on both the algorithms and the training material. This essay considers the natural statistics of infant- and toddler-egocentric vision. These natural training sets for human visual object recognition are very different from the training data fed into machine vision systems. Rather than equal experiences with all kinds of things, toddlers experience extremely skewed distributions with many repeated occurrences of a very few things. And though highly variable when considered as a whole, individual views of things are experienced in a specific order - with slow, smooth visual changes moment-to-moment, and developmentally ordered transitions in scene content. We propose that the skewed, ordered, biased visual experiences of infants and toddlers are the training data that allow human learners to develop a way to recognize everything, both the pervasively present entities and the rarely encountered ones. The joint consideration of real-world statistics for learning by researchers of human and machine learning seems likely to bring advances in both disciplines.
视觉学习既依赖于算法，也依赖于训练材料。本文探讨婴幼儿以自我为中心的视觉的自然统计规律。这些用于人类视觉物体识别的自然训练集与输入机器视觉系统的训练数据有很大不同。幼儿经历的并非是对各类事物的均等体验，而是极少数事物大量重复出现所导致的极端偏态分布。并且，尽管从整体来看事物的个体视图变化很大，但它们是按照特定顺序被体验的——即瞬间的视觉变化缓慢且平稳，场景内容在发育过程中按顺序转变。我们认为，婴幼儿这种偏态、有序且有偏向性的视觉体验就是训练数据，正是这些数据使得人类学习者能够形成一种识别一切事物的方法，包括普遍存在的实体和极少遇到的实体。人类和机器学习领域的研究人员共同考虑学习中的现实世界统计规律，似乎有望在这两个学科上都取得进展。
