a developmental approach to machine learning?

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ID: 179392
2017
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Abstract
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.
Reference Key
smith2017frontiersa Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Linda B. Smith;Lauren K. Slone
Journal accounts of chemical research
Year 2017
DOI
10.3389/fpsyg.2017.02124
URL
http://journal.frontiersin.org/article/10.3389/fpsyg.2017.02124/full
Keywords
schizophrenia development enamel genetics mutation latency phenotype object recognition

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