Automatic Colorization with Deep Convolutional Generative Adversarial Networks
We attempt to use DCGANs (deep convolutional generative adversarial nets) to tackle the automatic colorization of black and white photos to combat the tendency for vanilla neural nets to "average out" the results. We construct a small feed forward convolutional neural network as a baseline colorization system. We train the baseline model on the CIFAR 10 dataset with a per pixel Euclidean loss function on the chrominance values and achieve sensible but mediocre results. We propose using the adversarial framework as proposed by Goodfellow et al. [5] as an alternative to the loss function we reformulate the baseline model as a generator model that maps grayscale images and random noise input to the color image space, and construct a discriminator model that is trained to predict the probability that a given colorization was sampled from data distribution rather than generated by the generator model, conditioned on the grayscale image. We analyze the challenges that stand in the way of training adversarial networks, and suggest future steps to test the viability of the model.