Towards Multi-Scale Visual Explainability for Convolutional Neural Networks
Explainability methods seek to find out visual explanations for neural network decisions. Existing techniques mainly fall into two categories: backpropagation- based methods and occlusion-based methods. The former category selectively highlights the computed gradients, while the latter occludes the...
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| Format: | Others |
| Language: | English |
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KTH, Skolan för elektroteknik och datavetenskap (EECS)
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281359 |