US 12,307,739 B2
Image data synthesis using adversarial autoencoders for continual training of models
Satish Kumar Mopur, Bangalore (IN); Gunalan Perumal Vijayan, Bangalore (IN); and Krishnaprasad Lingadahalli Shastry, Bangalore (IN)
Assigned to Hewlett Packard Enterprise Development LP, Spring, TX (US)
Filed by HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP, Houston, TX (US)
Filed on Mar. 1, 2022, as Appl. No. 17/683,816.
Prior Publication US 2023/0281958 A1, Sep. 7, 2023
Int. Cl. G06V 10/00 (2022.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01); G06V 10/762 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/762 (2022.01) [G06N 3/08 (2013.01); G06N 7/01 (2023.01); G06V 10/82 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a processor; and
a memory configured to store instructions that, when executed by the processor, cause the processor to:
generate, for a stream of images to be processed by a model, a multi-dimensional representation of the stream of images that relates features of at least one image of the stream of images to features of at least one other image of the stream of images;
identify, based on the multi-dimensional representation, unknown images;
generate clusters of known images and at least one cluster of the unknown images based on the multi-dimensional representation of the stream of images, wherein the at least one cluster of unknown images corresponds to the unknown images;
create synthetic images based on the identified unknown images, wherein creating the synthetic images comprises:
identifying cluster densities for the cluster of unknown images;
weighting the cluster densities in proportion to a density count of the cluster densities; and
generating the synthetic images based on the weighted cluster densities of unknown images; and
retrain the model with training data including the synthetic images.