This approach can potentially improve the accuracy and efficiency of image classification tasks, 8 especially in scenarios where a limited amount of training data is available. Furthermore, the use 9 of Gabor filters can provide insights into the underlying structure and texture of images, which 10 can aid in developing better feature extraction techniques. Future research can explore the use 11 of Gabor filters in other deep learning architectures such as autoencoders and generative models. 12 Overall, this study highlights the potential benefits of incorporating domain-specific knowledge 13 into deep learning models to enhance their performance.
