  Convolutional Neural Networks (CNNs) are known for their ability to learn
hierarchical structures, naturally developing detectors for objects, and
semantic concepts within their deeper layers. Activation maps (AMs) reveal
these saliency regions, which are crucial for many Explainable AI (XAI)
methods. However, the direct exploitation of raw AMs in CNNs for feature
attribution remains underexplored in literature. This work revises Class
Activation Map (CAM) methods by introducing the Label-free Activation Map
(LaFAM), a streamlined approach utilizing raw AMs for feature attribution
without reliance on labels. LaFAM presents an efficient alternative to
conventional CAM methods, demonstrating particular effectiveness in saliency
map generation for self-supervised learning while maintaining applicability in
supervised learning scenarios.
