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Learning Compositional Representations for Few-Shot Recognition
[article]
2019
arXiv
pre-print
One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from just a few examples. Instrumental to this rapid learning ability is the compositional structure of concept representations in the human brain --- something that deep learning models are lacking. In this work, we make a step towards bridging this gap between human and machine learning by introducing a simple
arXiv:1812.09213v3
fatcat:dtoimu7qy5bgjp6ppfndsder4i