Resolution Learning in Deep Convolutional Networks Using Scale-Space Theory

Resolution in deep convolutional neural networks (CNNs) is typically bounded by the receptive field size through filter sizes, and subsampling layers or strided convolutions on feature maps. The optimal resolution may vary significantly depending on the dataset. Modern CNNs hard-code their resolutio...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 01., Seite 8342-8353
1. Verfasser:	Pintea, Silvia L (VerfasserIn)
Weitere Verfasser:	Tomen, Nergis, Goes, Stanley F, Loog, Marco, van Gemert, Jan C
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2021
Zugriff auf das übergeordnete Werk:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:	Journal Article