Poster
in
Affinity Workshop: LatinX in AI (LXAI) Workshop
Gabor Filters as Initializers for Convolutional Neural Networks: A Study on Inductive Bias and Performance on Image Classification
Pablo Rivas · Mehang Rai
Keywords: [ convolutional neural networks ] [ Deep Learning ] [ Computer Vision ] [ Machine Learning ]
This study explores the impact of Gabor filters on Convolutional Neural Networks (CNNs) performance in image classification tasks. Prior research has indicated that the receptive filters of CNNs often resemble Gabor filters, suggesting their potential as initial receptive filters. We conducted an extensive analysis on various general object datasets, demonstrating that integrating Gabor filters in the receptive layer enhances CNN performance, as evidenced by improved accuracy, higher Area Under the Curve (AUC), and reduced loss. Furthermore, our findings suggest that CNNs equipped with Gabor filters in the receptive layer can perform better in a shorter training period than traditional random initialization techniques.