Colorizing images with Conditional Adversarial Networks and Transfer Learning

Automatic image colorization is an image-to-image translation problem where a model is able to automatically convert a one-dimensional grayscale image to a three-dimensional color image. Inspiration is taken from the Pix2Pix image-to-image translation model and then a pre-trained MobileNet model is integrated into the generator of the Conditional Generative Adversarial Network (cGAN). The goal is to use transfer learning for the purpose of achieving satisfactory results with less training data while at the same time decreasing training time and improving performance. Moreover, quantitative and qualitative analysis is performed. The results show that by using transfer learning the modified model achieves a PSNR score of 24.21 and a SSIM score of 0.9374 and with that improves upon the baseline.