KPCA-CAM: Visual Explainability of Deep Computer Vision Models using Kernel PCA Publication date: 30 Sep 2024 Topic: Image Classification Paper: https://arxiv.org/pdf/2410.00267v1.pdf GitHub: https://github.com/jacobgil/pytorch-grad-cam Description: This research introduces KPCA-CAM, a technique designed to enhance the interpretability of Convolutional Neural Networks (CNNs) through improved class activation maps. KPCA-CAM leverages Principal Component Analysis (PCA) with the kernel trick to capture nonlinear relationships within CNN activations more effectively. By mapping data into higher-dimensional spaces with kernel functions and extracting principal components from this transformed hyperplane, KPCA-CAM provides more accurate representations of the underlying data manifold. This enables a deeper understanding of the features influencing CNN decisions.