Initialization of convolutional neural networks by Gabor filters

In transfer learning, for a given classification task, the learning from source domain into target domain is achieved by training/transferring a pre-trained network with data from target domain. During this process, a pre-trained network is a pre-requisite for transferring the knowledge from source domain into the target domain. In this study, to eliminate the need for such a pre-trained model, Gabor filters are utilized. In the proposed method, a Convolutional Neural Network is constructed by initializing its first convolutional layer, which represents the low-level features, such as corners and edges, with Gabor filters that have similar low-level characteristics. Experimental results on MNIST, CIFAR-10, and CIFAR-100 datasets show that Gabor filters based initialization of the network has similar characteristics with model transfer and can be applied for transfer learning without using a pre-trained model.