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. https://www.roneverhart.com/Dubble-Bubble-Gumball-Machine-Dispensers-12-Piece-Box/
Leveraging Prior Concept Learning Improves Generalization From Few Examples in Computational Models of Human Object Recognition
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