Abstract: The domain adaptation problem is addressed by using the predictions of a trained model over both source and target domain to retain the model with the assistance of an auxiliary model and a modified objective function. Inaccuracy in the model's predictions in the target domain is treated as noise and is reduced by using a robust learning framework during retraining, enabling unsupervised training in the target domain. Applications include object detection models, where noise in retraining is reduced by explicitly representing label noise and geometry noise in the objective function and using the ancillary model to inject information about label noise.

Abstract: The domain adaptation problem is addressed by using the predictions of a trained model over both source and target domain to retain the model with the assistance of an auxiliary model and a modified objective function. Inaccuracy in the model's predictions in the target domain is treated as noise and is reduced by using a robust learning framework during retraining, enabling unsupervised training in the target domain. Applications include object detection models, where noise in retraining is reduced by explicitly representing label noise and geometry noise in the objective function and using the ancillary model to inject information about label noise.

Abstract: Methods, apparatuses, and computer-readable mediums for generating human action data sets are disclosed by the present disclosure. In an aspect, an apparatus may receive a set of reference images, where each of the images within the set of reference images includes a person, and a background image. The apparatus may identify body parts of the person from the set of reference image and generate a transformed skeleton image by mapping each of the body parts of the person to corresponding skeleton parts of a target skeleton. The apparatus may generate a mask of the transformed skeleton image. The apparatus may generate, using machine learning, a frame of the person formed according to the target skeleton within the background image.

Abstract: Methods, apparatuses, and computer-readable mediums for generating human action data sets are disclosed by the present disclosure. In an aspect, an apparatus may receive a set of reference images, where each of the images within the set of reference images includes a person, and a background image. The apparatus may identify body parts of the person from the set of reference image and generate a transformed skeleton image by mapping each of the body parts of the person to corresponding skeleton parts of a target skeleton. The apparatus may generate a mask of the transformed skeleton image. The apparatus may generate, using machine learning, a frame of the person formed according to the target skeleton within the background image.