  The Gaussian kernel and its derivatives have already been employed for
Convolutional Neural Networks in several previous works. Most of these papers
proposed to compute filters by linearly combining one or several bases of fixed
or slightly trainable Gaussian kernels with or without their derivatives. In
this article, we propose a high-level configurable layer based on anisotropic,
oriented and shifted Gaussian derivative kernels which generalize notions
encountered in previous related works while keeping their main advantage. The
results show that the proposed layer has competitive performance compared to
previous works and that it can be successfully included in common deep
architectures such as VGG16 for image classification and U-net for image
segmentation.
