Color Invariant Convolution (CIConv) is a learnable Convolutional Neural Network (CNN) layer that reduces the distribution shift between the source and target set in the CNN under an illumination-based domain shift. We explore the semantic segmentation performance for daynight domain adaptation when using CIConv. We will test this on two settings: one with only labeled train data available and one with access to both labeled training data and unlabeled test data. In both settings, we will cast an invariant edge detector as a trainable CIConv layer in the CNN to transform the daytime dataset to a domain invariant representation. We will execute day-night domain adaptation and evaluate the mean Intersection over Union over the results. We compare this result to the vanilla version of the same code without using the invariant edge detector as a trainable layer. We will discuss the results obtained from our experiments and show that the trainable CIConv layer does not always result in better outcomes for day-night domain adaptation.