A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition
2021
Frontiers in Computational Neuroscience
Humans quickly and accurately learn new visual concepts from sparse data, sometimes just a single example. The impressive performance of artificial neural networks which hierarchically pool afferents across scales and positions suggests that the hierarchical organization of the human visual system is critical to its accuracy. These approaches, however, require magnitudes of order more examples than human learners. We used a benchmark deep learning model to show that the hierarchy can also be
doi:10.3389/fncom.2020.586671
pmid:33510629
pmcid:PMC7835122
fatcat:25sn7lntlbcxzl4ef3kfizd5ju