US 12,444,183 B2
Modeling disjoint manifolds
Jesse Cole Cresswell, Toronto (CA); Brendan Leigh Ross, Toronto (CA); Anthony Lawrence Caterini, Toronto (CA); Gabriel Loaiza Ganem, Toronto (CA); and Bradley Craig Anderson Brown, Oakville (CA)
Assigned to The Toronto-Dominion Bank, Toronto (CA)
Filed by THE TORONTO-DOMINION BANK, Toronto (CA)
Filed on May 26, 2023, as Appl. No. 18/202,455.
Claims priority of provisional application 63/350,340, filed on Jun. 8, 2022.
Claims priority of provisional application 63/346,815, filed on May 27, 2022.
Prior Publication US 2023/0386190 A1, Nov. 30, 2023
Int. Cl. G06V 10/82 (2022.01); G06V 10/762 (2022.01)
CPC G06V 10/82 (2022.01) [G06V 10/7625 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A system for a training a generative model of data on disjoint manifolds, comprising:
one or more processors;
one or more non-transitory computer-readable media containing instructions for execution by the one or more processors for:
identifying a plurality of training samples for which to train a generative model;
grouping the plurality of training samples to a plurality of groups;
generating a plurality of generative sub-models corresponding to a number of the plurality of groups by, for each group of the plurality of groups:
identifying a sampling frequency for sampling the sub-model based on a number of training samples associated with the group relative to the plurality of training samples; and
training a generative sub-model for the group based on the training samples of the group; and
storing the generative model as the plurality of generative sub-models and the associated sampling frequency for each sub-model.