We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers improved visual explanations, when compared to existing alternatives. The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
|Title of host publication||CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management|
|Publisher||Association for Computing Machinery|
|Number of pages||10|
|State||Published - 26 Oct 2021|
|Event||30th ACM International Conference on Information and Knowledge Management, CIKM 2021 - Virtual, Online, Australia|
Duration: 1 Nov 2021 → 5 Nov 2021
|Name||International Conference on Information and Knowledge Management, Proceedings|
|Conference||30th ACM International Conference on Information and Knowledge Management, CIKM 2021|
|Period||1/11/21 → 5/11/21|
Bibliographical notePublisher Copyright:
© 2021 ACM.
- deep learning
- explainable & interpretable ai
- saliency maps