A method performed by one or more data processing apparatus, the method comprising: providing an input that comprises a provided image to a generative neural network having a plurality of generative neural network parameters, wherein: the generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image; the extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be an extension of the provided image; the generative neural network is jointly trained with a discriminative neural network having a plurality of discriminative neural network parameters that is configured to process a given image to generate a discriminative output characterizing a likelihood that the given image was generated using the generative neural network; the generative neural network has been trained using an adversarial loss objective function that depends on the discriminative output; training the generative neural network using the adversarial loss objective function comprises processing a training input that comprises a training image using the generative neural network; and the discriminative neural network is conditioned on a semantic feature representation of a target image, the training image being a cropped representation of the target image, wherein the discriminative neural network is conditioned on the semantic feature representation of the target image by either: providing the semantic feature representation of the target image as an additional input to the discriminative neural network; or determining the discriminative output based at least in part on a similarity measure between the semantic representation of a target extended image and an intermediate output of the discriminative neural network, and wherein the semantic representation of the target extended image is normalized by a normalization engine prior to using it to condition the discriminative neural network. the generative neural network processes the input in accordance with trained values of the plurality of generative neural network parameters to generate an extended image; the extended image has (i) more rows, more columns, or both than the provided image, and (ii) is predicted to be an extension of the provided image; the generative neural network is jointly trained with a discriminative neural network having a plurality of discriminative neural network parameters that is configured to process a given image to generate a discriminative output characterizing a likelihood that the given image was generated using the generative neural network; the generative neural network has been trained using an adversarial loss objective function that depends on the discriminative output; training the generative neural network using the adversarial loss objective function comprises processing a training input that comprises a training image using the generative neural network; and the discriminative neural network is conditioned on a semantic feature representation of a target image, the training image being a cropped representation of the target image, wherein the discriminative neural network is conditioned on the semantic feature representation of the target image by either: providing the semantic feature representation of the target image as an additional input to the discriminative neural network; or determining the discriminative output based at least in part on a similarity measure between the semantic representation of a target extended image and an intermediate output of the discriminative neural network, and providing the semantic feature representation of the target image as an additional input to the discriminative neural network; or determining the discriminative output based at least in part on a similarity measure between the semantic representation of a target extended image and an intermediate output of the discriminative neural network, and wherein the semantic representation of the target extended image is normalized by a normalization engine prior to using it to condition the discriminative neural network.