公开(公告)号：US20250148644A1

公开(公告)日：2025-05-08

申请号：US18737459

申请日：2024-06-07

Applicant: Amazon Technologies, Inc.

Inventor： Andrew C. Mihal ,  Steven L. Teig

IPC: G06T7/80 ,  G06N3/04 ,  G06N3/042 ,  G06N3/08 ,  G06N3/084 ,  G06T7/521 ,  G06T7/55 ,  G06T7/593 ,  G06V20/58 ,  G08G1/16 ,  H04N13/239 ,  H04N13/246

Abstract: Some embodiments of the invention provide a novel method for training a multi-layer node network. Some embodiments train the multi-layer network using a set of inputs generated with random misalignments incorporated into the training data set. In some embodiments, the training data set is a synthetically generated training set based on a three-dimensional ground truth model as it would be sensed by a sensor array from different positions and with different deviations from ideal alignment and placement. Some embodiments dynamically generate training data sets when a determination is made that more training is required. Training data sets, in some embodiments, are generated based on training data sets for which the multi-layer node network has produced bad results.

公开(公告)号：US12248880B2

公开(公告)日：2025-03-11

申请号：US18238507

申请日：2023-08-27

Applicant: Amazon Technologies, Inc.

Inventor： Eric A. Sather ,  Steven L. Teig ,  Andrew C. Mihal

IPC: G06N3/084 ,  G06F18/21 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00 ,  G06V10/44 ,  G06V10/764 ,  G06V10/82 ,  G06V40/16

Abstract: Some embodiments provide a method for training a machine-trained (MT) network that processes inputs using network parameters. The method propagates a set of input training items through the MT network to generate a set of output values. The set of input training items comprises multiple training items for each of multiple categories. The method identifies multiple training item groupings in the set of input training items. Each grouping includes at least two training items in a first category and at least one training item in a second category. The method calculates a value of a loss function as a summation of individual loss functions for each of the identified training item groupings. The individual loss function for each particular training item grouping is based on the output values for the training items of the grouping. The method trains the network parameters using the calculated loss function value.