  The Convolutional Neural Network (CNN) is a widely used deep learning
architecture for computer vision. However, its black box nature makes it
difficult to interpret the behavior of the model. To mitigate this issue, AI
practitioners have explored explainable AI methods like Class Activation Map
(CAM) and Grad-CAM. Although these methods have shown promise, they are limited
by architectural constraints or the burden of gradient computing. To overcome
this issue, Score-CAM and Ablation-CAM have been proposed as gradient-free
methods, but they have longer execution times compared to CAM or Grad-CAM based
methods, making them unsuitable for real-world solution though they resolved
gradient related issues and enabled inference mode XAI. To address this
challenge, we propose a fast gradient-free Reciprocal CAM (Recipro-CAM) method.
Our approach involves spatially masking the extracted feature maps to exploit
the correlation between activation maps and network predictions for target
classes. Our proposed method has yielded promising results, outperforming
current state-of-the-art method in the Average Drop-Coherence-Complexity (ADCC)
metric by $1.78 \%$ to $3.72 \%$, excluding VGG-16 backbone. Moreover,
Recipro-CAM generates saliency maps at a similar rate to Grad-CAM and is
approximately $148$ times faster than Score-CAM. The source code for
Recipro-CAM is available in our data analysis framework.
