Abstract: Implementations are described herein for improving unsupervised domain adaptation (UDA) by using improved adaptive teacher for object detection with cross-domain mix-up. In various implementations, cross-domain training of an object detection machine learning model may include: performing weak augmentation on images from a target domain DT to generate a first set of weakly augmented target domain images; perform strong augmentation on images from the source domain DS and images from the target domain DT to generate a second set of strongly augmented images; processing the second set of strongly augmented images to generate a third set of inter-domain mixes of the images from DS and DT; and jointly train the object detection machine learning model, as a student machine learning model, with a teacher machine learning model using the first and third sets.

Abstract: Implementations are described herein for improving unsupervised domain adaptation (UDA) by using improved adaptive teacher for object detection with cross-domain mix-up. In various implementations, cross-domain training of an object detection machine learning model may include: performing weak augmentation on images from a target domain DT to generate a first set of weakly augmented target domain images; perform strong augmentation on images from the source domain DS and images from the target domain DT to generate a second set of strongly augmented images; processing the second set of strongly augmented images to generate a third set of inter-domain mixes of the images from DS and DT; and jointly train the object detection machine learning model, as a student machine learning model, with a teacher machine learning model using the first and third sets.