公开(公告)号：US12288393B2

公开(公告)日：2025-04-29

申请号：US17798969

申请日：2021-06-04

Applicant: Google LLC

Inventor： Rudy Bunel ,  Jim Huibrecht Winkens ,  Abhijit Guha Roy ,  Olaf Ronneberger ,  Seyed Mohammadali Eslami ,  Ali Taylan Cemgil ,  Simon Kohl

IPC: G06V10/82 ,  G06T7/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a neural network to (i) generate accurate network outputs for a machine learning task and (ii) generate intermediate outputs that can be used to reliably classify out-of-distribution inputs. In one aspect, a method comprises: training the neural network using supervised and contrastive losses, comprising repeatedly performing operations including: obtaining first and second network inputs; processing each network input using the neural network to generate its respective network input embedding; processing the first network input using the neural network to generate a network output; and adjusting the network parameter values using supervised and contrastive loss gradients, wherein: the supervised loss is based on: (i) the network output, and (ii) a corresponding target network output; and the contrastive loss is based on at least: (i) the first network input embedding, and (ii) the second network input embedding.

公开(公告)号：US20240169272A1

公开(公告)日：2024-05-23

申请号：US18551847

申请日：2022-02-07

Applicant: Google LLC

Inventor： Patricia MacWilliams ,  Abhijit Guha Roy ,  Jim Winkens ,  Alan Karthikesalingam ,  Jie Ren ,  Balaji Lakshminarayanan

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A method includes determining, by a machine learning model and based on input data, a feature map that represents learned features present in the input data. The method also includes, for each respective inlier class of a plurality of inlier classes, determining, by the machine learning model and based on the feature map, a corresponding inlier score indicative of a probability that the input data belongs to the respective inlier class. The method additionally includes, for each respective outlier class of a plurality of outlier classes, determining, by the machine learning model and based on the feature map, a corresponding outlier score indicative of a probability that the input data belongs to the respective outlier class. The method further includes determining, based on the inlier scores and the outlier scores, whether the input data corresponds to the plurality of inlier classes or to the plurality of outlier classes.