Color Space Transformation Network

Deep networks have become very popular over the past few years. The main reason for this widespread use is their excellent ability to learn and predict knowledge in a very easy and efficient way. Convolutional neural networks and auto-encoders have become the normal in the area of imaging and computer vision achieving unprecedented accuracy levels in many applications. The most common strategy is to build and train networks with many layers by tuning their hyper-parameters. While this approach has proven to be a successful way to build robust deep learning schemes it suffers from high complexity. In this paper we introduce a module that learns color space transformations within a network. Given a large dataset of colored images the color space transformation module tries to learn color space transformations that increase overall classification accuracy. This module has shown to increase overall accuracy for the same network design and to achieve faster convergence. It is part of a broader family of image transformations (e.g. spatial transformer network).