Training machine learning models based on unlabeled data

A system comprising:

• a memory to store data;
• and
• a processing device, operatively coupled to the memory, to:
• obtain a set of images, wherein:
• the set of images comprises a first subset and a second subset;
• and
• the first subset is associated with a first set of reference labels;
• generate a set of pseudo labels for the set of images and a second set of labels for the second subset based on the first subset, the second subset, a first machine learning model, and a domain adaption model;
• generate second machine learning model, wherein:
• the second machine learning model is generated based on the set of images, the set of pseudo labels, the first set of reference labels, and the second set of labels;
• and
• the second set of labels is updated based on one or more inferences generated by the second machine learning model.

A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processing device, cause the processing device to:

• obtain a set of images, wherein:
• the set of images comprises a first subset and a second subset;
• and
• the first subset is associated with a first set of reference labels;
• generate a set of pseudo labels for the set of images and a second set of labels for the second subset based on the first subset, the second subset, a first machine learning model, and a domain adaption model;
• generate second machine learning model, wherein:
• the second machine learning model is generated based on the set of images, the set of pseudo labels, the first set of reference labels, and the second set of labels;
• and
• the second set of labels is updated based on one or more inferences generated by the second machine learning model.