Interpretable explanations of black boxes by meaningful perturbation
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been dev...
| Huvudupphovsmän: | , |
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| Materialtyp: | Conference item |
| Språk: | English |
| Publicerad: |
IEEE
2017
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