ニューラルネットワークのニューラル化
1Save Sight Institute, University of Sydney, Sydney, NSW 2000, Australia.
Abstract:
Deep neural networks (DNNs) are practical and effective but, despite the name, they lack biological validity. The recent study by Kang et al.1 in Patterns takes a step toward rectifying this deficit by hard-wiring receptive fields into the first layer of a visual DNN, and the authors show that their network can generalize across image types. Training on photographs, for example, resulted in good performance on sketches; conventional DNNs did not match this behavior.
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