Overview
pix2pix is an image-to-image translation framework leveraging conditional adversarial networks (cGANs). It learns a mapping from input image to output image while simultaneously learning a loss function to train this mapping. This allows the same generic approach to be applied to diverse problems traditionally requiring different loss formulations. The architecture consists of a generator and a discriminator, trained adversarially. The generator aims to create realistic images from the input, while the discriminator tries to distinguish between generated and real images. This approach allows pix2pix to perform tasks such as synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images. The tool is available in PyTorch, Torch, Tensorflow, Chainer, Keras, and Wolfram Cloud implementations.