公开(公告)号：WO2022103993A1

公开(公告)日：2022-05-19

申请号：PCT/US2021/059030

申请日：2021-11-11

Applicant: GOOGLE LLC

Inventor： SOHN, Kihyuk ,  LI, Chun-Liang ,  YOON, Jinsung ,  PFISTER, Tomas, Jon

IPC: G06V10/82 ,  G06V10/774

Abstract: A method (500) for training a machine learning model (150) includes obtaining a set of training samples (112). For each training sample in the set of training samples, during each of one or more training iterations, the method includes cropping the training sample to generate a first cropped image (140 A), cropping the training sample to generate a second cropped image (140B) that is different than the first cropped image, and duplicating a first portion (210) of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion (220) of the second cropped image to form an augmented second cropped image (140BA). The first portion is different than the second portion. The method also includes training the machine learning model with the first cropped image and the augmented second cropped image.

公开(公告)号：WO2023086909A1

公开(公告)日：2023-05-19

申请号：PCT/US2022/079674

申请日：2022-11-10

Applicant: GOOGLE LLC

Inventor： SOHN, Kihyuk ,  YOON, Jinsung ,  LI, Chun-Liang ,  PFISTER, Tomas Jon ,  LEE, Chen-Yu

IPC: G06F18/214 ,  G06N3/0895 ,  G06V10/50 ,  G06V10/762 ,  G06V10/82 ,  G06V10/74 ,  G06V10/44

Abstract: A computer-implemented method (500) includes receiving an anomaly clustering request (20) that requests data processing hardware (144) to assign each image (152) of a plurality of images into one of a plurality of groups (302). The method also includes obtaining a plurality of images. For each respective image, the method includes extracting a respective set of patch embeddings (212) from the respective image, determining a distance (212) between the respective set of patch embeddings and each other set of patch embeddings, and assigning the respective image into one of the plurality of groups using the distances between the respective set of patch embeddings and each other set of patch embeddings.

公开(公告)号：WO2022251462A1

公开(公告)日：2022-12-01

申请号：PCT/US2022/031087

申请日：2022-05-26

Applicant: GOOGLE LLC

Inventor： LI, Chun-liang ,  YOON, Jinsung ,  SOHN, Kihyuk ,  ARIK, Sercan, Omer

IPC: G06N20/20 ,  G06N3/04

Abstract: Aspects of the disclosure provide for methods, systems, and apparatus, including computer- readable storage media, for anomaly detection using a machine learning framework trained entirely on unlabeled training data including both anomalous and non-anomalous training examples. A self-supervised one-class classifier (STOC) refines the training data to exclude anomalous training examples, using an ensemble of machine learning models. The ensemble of models are retrained on the refined training data. The STOC can also use the refined training data to train a representation learning model to generate one or more feature values for each training example, which can be processed by the trained ensemble of models and eventually used for training an output classifier model to predict whether input data is indicative of anomalous or non-anomalous data.