There and back again: revisiting backpropagation saliency methods
Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample. A popular class of such methods is based on backpropagating a signal and analyzing the resulting gradient. Despite much research on such methods, relatively little work has been done...
| Main Authors: | , , , |
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| 格式: | Conference item |
| 語言: | English |
| 出版: |
IEEE
2020
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