US 12,406,469 B1
Class-incremental learning with pretrained machine learning models
Avinash Aghoram Ravichandran, Seattle, WA (US); Pashmeen Mistry, San Jose, CA (US); Tz-Ying Wu, San Diego, CA (US); Gurumurthy Swaminathan, Redmond, WA (US); Zhizhong Li, Seattle, WA (US); Rahul Bhotika, Bellevue, WA (US); and Stefano Soatto, Pasadena, CA (US)
Assigned to Amazon Technologies, Inc., Seattle, WA (US)
Filed by Amazon Technologies, Inc., Seattle, WA (US)
Filed on Sep. 23, 2022, as Appl. No. 17/951,887.
Int. Cl. G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/80 (2022.01); G06V 20/70 (2022.01)
CPC G06V 10/764 (2022.01) [G06V 10/774 (2022.01); G06V 10/809 (2022.01); G06V 20/70 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
receiving, at a machine learning (ML) service of a provider network, a first request to label objects belonging to a first object class in a first set of images;
receiving, at the ML service, the first set of images;
labeling, using a first ML model hosted at the ML service, objects belonging to the first object class in the first set of images;
receiving, at the ML service, a second request to label objects belonging to a second object class;
copying, by the ML service, a proper subset of the first ML model to generate a base branch and a novel branch;
receiving, at the ML service, a second set of images including images of objects belonging to the second object class;
training, by the ML service using the second set of images, the novel branch to generate a second ML model including the base branch and the novel branch;
receiving, at the ML service, a third request to label objects belonging to the first and second object classes in a third set of images;
receiving, at the ML service, the third set of images; and
labeling, using the second ML model hosted at the ML service, objects belonging to the first and second object classes in the third set of images.