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Poster for IMAGE ATTRIBUTION BY GENERATING IMAGES

DOI:
10.60864/ewss-7962
Citation Author(s):
Submitted by:
aniket singh
Last updated:
6 June 2024 - 10:27am
Document Type:
Poster
Document Year:
2024
Event:
Presenters:
aniket singh
Paper Code:
4010
Categories:
 

We introduce GPNN-CAM, a novel method for CNN explanation, that bridges two distinct areas of computer vision:
Image Attribution, which aims to explain a predictor by highlighting image regions it finds important, and Single
Image Generation (SIG), that focuses on learning how to generate variations of a single sample. GPNN-CAM leverages samples generated by Generative
Patch Nearest Neighbors (GPNN) into a Class Activation Map (CAM) flavored attribution scheme. Our findings reveal that the incorporation of these samples yields remarkably effective results, enabling GPNN-CAM to demonstrate superior performance across multiple classifier architectures, and datasets.

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