Abstract: A computer-implemented system and method include performing neural style transfer augmentations using at least a content image, a first style image, and a second style image. A first augmented image is generated based at least on content of the content image and a first style of the first style image. A second augmented image is generated based at least on the content of the content image and a second style of the second style image. The machine learning system is trained with training data that includes at least the content image, the first augmented image, and the second augmented image. A loss output is computed for the machine learning system. The loss output includes at least a consistency loss that accounts for a predicted label provided by the machine learning system with respect to each of the content image, the first augmented image, and the second augmented image.

Abstract: A computer-implemented system and method provide improved training to a machine learning system, such as a vision transformer. The system and method include performing neural style transfer augmentations using at least a content image, a first style image, and a second style image. A first augmented image is generated based at least on content of the content image and a first style of the first style image. A second augmented image is generated based at least on the content of the content image and a second style of the second style image. The machine learning system is trained with training data that includes at least the content image, the first augmented image, and the second augmented image. A loss output is computed for the machine learning system. The loss output includes at least a consistency loss that accounts for a predicted label provided by the machine learning system with respect to each of the content image, the first augmented image, and the second augmented image.