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A little labeling goes a long way: Semi-supervised learning in infancy
Alexander LaTourrette1, Sandra R Waxman1,2
1Department of Psychology, Northwestern University, Evanston, Illinois.
Developmental Science
|August 30, 2018
Summary
Infants can learn object categories using semi-supervised learning, where only a few examples are labeled. This learning method mirrors how babies acquire real-world categories and their names.
Area of Science:
- Cognitive Science
- Developmental Psychology
- Machine Learning
Background:
- Labeling aids infant object categorization.
- Most real-world object exposure for infants is unlabeled.
- Machine learning offers insights into learning from sparse data.
Purpose of the Study:
- To investigate if infants utilize semi-supervised learning for object categorization.
- To determine the impact of labeling timing on infant category learning.
- To provide evidence for semi-supervised learning in infancy.
Main Methods:
- A novelty preference task was used with 96 two-year-old infants.
- Infants were familiarized with a novel object category under different labeling conditions.
- Conditions included fully supervised, semi-supervised, unsupervised, and reversed semi-supervised learning.
Main Results:
- Infants succeeded in both fully supervised and semi-supervised conditions.
- Infants failed to learn the category in the unsupervised condition.
- Learning failed when labels were presented at the end of familiarization (reversed semi-supervised).
Conclusions:
- Infants demonstrate semi-supervised learning capabilities.
- Early labeling is crucial for infants to initiate categorization.
- Infants effectively learn categories from typical, sparsely labeled real-world input.
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