Abstract: Machine learning (ML) models are introduced for image stylization, which learn and apply multiple selectable image styles, including styles learned in an ad hoc fashion. According to some embodiments, such models may be trained on images or image pairs comprising images stylized into one or more of a plurality of predetermined styles. At inference time, a style vector representative of a particular selected style may be obtained and injected into the neural network at one or more locations to stylize an input image into the selected style. According to other embodiments, the neural network may be trained in an ad hoc fashion to learn new styles based on small sets of input images. Adversarial training (e.g., in the form of a discriminator network and/or conditional generative adversarial network (C-GAN) loss) may optionally be incorporated into the training to reduce artifacts and generate images that more closely match the selected style.

Abstract: Machine learning (ML) models are introduced for image stylization, which learn and apply multiple selectable image styles, including styles learned in an ad hoc fashion. According to some embodiments, such models may be trained on images or image pairs comprising images stylized into one or more of a plurality of predetermined styles. At inference time, a style vector representative of a particular selected style may be obtained and injected into the neural network at one or more locations to stylize an input image into the selected style. According to other embodiments, the neural network may be trained in an ad hoc fashion to learn new styles based on small sets of input images. Adversarial training (e.g., in the form of a discriminator network and/or conditional generative adversarial network (C-GAN) loss) may optionally be incorporated into the training to reduce artifacts and generate images that more closely match the selected style.