Exploiting the relationship between Kendall’s rank correlation and cosine similarity for attribution protection
Model attributions are important in deep neural networks as they aid practitioners in understanding the models, but recent studies reveal that attributions can be easily perturbed by adding imperceptible noise to the input. The non-differentiable Kendall's rank correlation is a key performan...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/161935 https://nips.cc/ |