Visualization Techniques Applied to Image-to-Image Translation

Abstract

Convolutional Neural Networks became a state-of-the-art approach for many different problems of computer vision, pattern recognition, and image processing. However, due to the large number of parameters of these architectures, researchers may find difficult to explain what the networks are using as discriminative patterns. An alternative to better understand the behavior of the learned convolutional kernels is the use of visualization techniques. Currently, visualization techniques are more frequently applied to classification tasks. In this paper, we address the visualization of image-to-image translation. One of the contributions of our work is the possibility to modify a network based on the kernel visualization and achieve superior results.