Taylor Series Kernelized Layer for Fine-Grained Recognition

In this paper, we propose a new architecture to enhance dense layers with a Taylor Series Kernelized Layer (TSKL). The proposed layer expands the underlying linear kernel of dense layers to a higher-order Taylor series kernel. This kernel is able to learn more complex patterns than the linear one and thus be more discriminative. In other words, TKSL first maps input data to a higher-dimensional Reproducing Kernel Hilbert Space (RKHS). After that, it learns a linear classifier in that RKHS which corresponds to a powerful non-linear classifier in the original feature space. The mapping features to a higher-order RKHS is performed implicitly by leveraging the kernel trick and explicitly by combining multiple kernels. The experimental results demonstrate that the proposed layer outperforms the ordinary dense layer when uses in both Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs).