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.