Loss-Based Attention for Interpreting Image-Level Prediction of Convolutional Neural Networks
Although deep neural networks have achieved great success on numerous large-scale tasks, poor interpretability is still a notorious obstacle for practical applications. In this paper, we propose a novel and general attention mechanism, loss-based attention, upon which we modify deep neural networks...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 14., Seite 1662-1675
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| 1. Verfasser: |
Shi, Xiaoshuang
(VerfasserIn) |
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| Weitere Verfasser: |
Xing, Fuyong,
Xu, Kaidi,
Chen, Pingjun,
Liang, Yun,
Lu, Zhiyong,
Guo, Zhenhua |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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| Schlagworte: | Journal Article |
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