公开(公告)号：US11741372B2

公开(公告)日：2023-08-29

申请号：US17397677

申请日：2021-08-09

Applicant: salesforce.com, inc.

Inventor： Lily Hu ,  Caiming Xiong ,  Richard Socher

IPC: G06F16/906 ,  G06N3/088 ,  G06N3/08 ,  G06F18/21 ,  G06F18/2413 ,  G06V10/764 ,  G06V10/776 ,  G06V10/80 ,  G06V10/82 ,  G06F16/55

CPC classification number: G06N3/088 ,  G06F16/55 ,  G06F16/906 ,  G06F18/217 ,  G06F18/24137 ,  G06N3/08 ,  G06V10/764 ,  G06V10/776 ,  G06V10/811 ,  G06V10/82

Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.

公开(公告)号：US11087177B2

公开(公告)日：2021-08-10

申请号：US16176075

申请日：2018-10-31

Applicant: salesforce.com, inc.

Inventor： Lily Hu ,  Caiming Xiong ,  Richard Socher

IPC: G06K9/62 ,  G06N3/08

Abstract: Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.