IRJET - mage Colorization using Self Attention GAN

This document summarizes research on using self-attention generative adversarial networks (GANs) for the task of image colorization. The researchers propose a model that uses a U-Net generator with self-attention and spectral normalization, and a discriminator trained using a two time-scale update rule. They find that initially the model produces high quality colorizations, but quality oscillates after a certain point in training. Comparisons show their model produces more realistic colors than previous methods, with fewer artifacts. Human evaluations also found their model's outputs were perceived as more reasonable. The researchers conclude self-attention GANs can produce plausible colorizations, and their training method helps shorten effective training time.