Method and system for adaptation of a trained object detection model to account for domain shift

Abstract

The present disclosure provides a method and system for adapting a machine learning model, such as an object detection model, to account for domain shift. The method includes receiving a labeled data elements and target image samples and performing a plurality of model adaptation epochs. Each adaptation epoch includes: predicting for each of the target image samples, using the machine learning model configured by a current set of configuration parameters, a corresponding target class label for the respective target data object included in the target image sample; generating a plurality of labeled mixed data elements that each include: (i) a mixed image sample including a source data object from one of the source image samples and a target data object from one of the target image samples, and (ii) the corresponding source class label for the source data object and the corresponding target class label for the target data object. The method also includes adjusting the current set of configuration parameters to minimize a loss function for the machine learning model for the plurality of mixed data elements. The method results in adapted machine learning model that accounts for domain shift and that has improved performance at inference on new target image samples.