An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - (2020) vom: 20. Jan.
1. Verfasser:	Gao, Xinjian (VerfasserIn)
Weitere Verfasser:	Mu, Tingting, Goulermas, John Y, Thiyagalingam, Jeyarajan, Wang, Meng
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2020
Zugriff auf das übergeordnete Werk:	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:	Journal Article