Abstract: The present disclosure relates to an image style transfer method and apparatus, an image style transfer model training method and apparatus, a device, and a medium. The image style transfer model training method includes: acquiring a first number of portrait image samples, training a preset neural network model by using the portrait image samples to determine a portrait image generation model and portrait model parameters; acquiring a second number of style image samples, training the portrait image generation model by using the style image samples to determine style model parameters, the second number being less than the first number; determining transfer model parameters based on the portrait model parameters and the style model parameters; and generating a first image style transfer model based on the transfer model parameters and the preset neural network model.

Abstract: In one embodiment, a method includes a computer system accessing a curvilinear image captured using a camera lens, generating multiple rectilinear images from the curvilinear image based at least in part on one or more calibration parameters associated with the camera lens, identifying semantic information in one or more of the rectilinear images by processing each of the multiple rectilinear images using a machine-learning model configured to identify semantic information in rectilinear images, and identifying semantic information in the curvilinear image based on the identified semantic information in the one or more rectilinear images.

Abstract: An image processing method and device. In the method, a vector to be edited in a latent space of an image generative network and a first target decision boundary of a first target attribute in the latent space are acquired. The first target attribute includes a first category and a second category, the latent space is divided into a first subspace and a second subspace by the first target decision boundary, the first target attribute of vectors in the first subspace is of the first category and the first target attribute of vectors in the second subspace is of the second category; the vector to be edited in the first subspace is moved to the second subspace, an edited vector is obtained; and the edited vector is input to the image generative network, and a target image is obtained.