US 12,243,295 B2
Robust neural network learning system
Wei Tong, Troy, MI (US); Jacob Alan Bond, Rochester Hills, MI (US); and Siddhartha Gupta, Rochester Hills, MI (US)
Assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed by GM Global Technology Operations LLC, Detroit, MI (US)
Filed on Mar. 31, 2022, as Appl. No. 17/709,553.
Prior Publication US 2023/0316728 A1, Oct. 5, 2023
Int. Cl. G06V 10/774 (2022.01); G06N 3/08 (2023.01); G06V 10/82 (2022.01)
CPC G06V 10/7747 (2022.01) [G06N 3/08 (2013.01); G06V 10/82 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A system comprising a computer including a processor and a memory, the memory including instructions such that the processor is programmed to:
receive a data constraint, a feature constraint, and an intermediate concept constraint at a neural network, wherein the data constraint includes a perturbed image of an object, the feature constraint includes a physical property of a vehicle sensor, and the intermediate concept constraint is based on human knowledge that provides additional context about a training data, the additional context comprising additional definitions and relationships pertaining to an object depicted within the training data; and
train the neural network with the training data, training labels, and the data constraint, the feature constraint, and the intermediate concept constraint.