The possibility to improve an existing method by making (part of) it learnable is explored in this research. The work that this research extends added prior knowledge to a Convolutional Neural Network (CNN) to improve its performance when dealing with an illumination shift. The method used for the preprocessing, is the color invariant. The method was used in a zero-domain adaptation setting, where the network is trained without having access to the target domain. The research demonstrated improved performance, motivating further improvements.

The Color Invariant Convolution (CIConv) layer implements the color invariant edge detectors. The layer converts the RGB input of each pixel to spectral differential quotients, which are used to determine the color invariant representation. This is done through two fixed linear transformations that only approximate these values. This indicates that an even better approximation can be obtained by making this transition learnable.

Two methods are used to make this transition learnable; a linear learning method and a non-linear learning method. The linear learning method uses the original transformation but allows for change and the non-linear method replaces the linear transform with a neural network. Both methods show potential for achieving better results than the fixed transformation, but only the linear learning method actually does perform better in a classification experiment. All experiments are done following the zero-shot day to night domain adaptation on a synthetic dataset.