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    Clustering Images for Anomaly Detection
    1.
    发明公开
    Clustering Images for Anomaly Detection 审中-公开

    公开(公告)号：US20230153980A1

    公开(公告)日：2023-05-18

    申请号：US18054524

    申请日：2022-11-10

    Applicant: Google LLC

    Inventor： Kihyuk Sohn ,  Jinsung Yoon ,  Chun-Liang Li ,  Tomas Jon Pfister ,  Chen-Yu Lee

    IPC: G06T7/00 ,  G06V10/762

    CPC classification number: G06T7/0004 ,  G06V10/7625 ,  G06V10/7635 ,  G06T2207/20081 ,  G06V10/764

    Abstract: A computer-implemented method includes receiving an anomaly clustering request that requests data processing hardware to assign each image of a plurality of images into one of a plurality of groups. The method also includes obtaining a plurality of images. For each respective image, the method includes extracting a respective set of patch embeddings from the respective image, determining a distance 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.

    Multi-Layer Perceptron Architecture For Times Series Forecasting
    3.
    发明公开
    Multi-Layer Perceptron Architecture For Times Series Forecasting 审中-公开

    公开(公告)号：US20240249192A1

    公开(公告)日：2024-07-25

    申请号：US18417556

    申请日：2024-01-19

    Applicant: Google LLC

    Inventor： Sercan Omer Arik ,  Si-An Chen ,  Nathanael Christian Yoder ,  Chun-Liang Li

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: The present disclosure provides an architecture for time series forecasting. The architecture is based on multi-layer perceptrons (MLPs), which involve stacking linear models with non-linearities between them. In this architecture, the time-domain MLPs and feature-domain MLPs are used to perform both time-domain and feature-domain operations in a sequential manner, alternating between them. In some examples, auxiliary data is used as input, in addition to historical data. The auxiliary data can include known future data points, as well as static information that does not vary with time. The alternation of time-domain and feature-domain operations using linear models allows the architecture to learn temporal patterns while leveraging cross-variate information to generate more accurate time series forecasts.

    Unsupervised Anomaly Detection With Self-Trained Classification
    4.
    发明申请
    Unsupervised Anomaly Detection With Self-Trained Classification 有权

    公开(公告)号：US20220391724A1

    公开(公告)日：2022-12-08

    申请号：US17825788

    申请日：2022-05-26

    Applicant: Google LLC

    Inventor： Jinsung Yoon ,  Kihyuk Sohn ,  Chun-Liang Li ,  Sercan Omer Arik

    IPC: G06N5/04 ,  G06F11/34

    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.

    Semi-Supervised Anomaly Detection Under Distribution Mismatch
    6.
    发明公开
    Semi-Supervised Anomaly Detection Under Distribution Mismatch 审中-公开

    公开(公告)号：US20230237260A1

    公开(公告)日：2023-07-27

    申请号：US18150277

    申请日：2023-01-05

    Applicant: Google LLC

    Inventor： Jinsung Yoon ,  Kihyuk Sohn ,  Chun-Liang Li ,  Sercan Omer Arik

    IPC: G06F40/216 ,  G06N5/022

    CPC classification number: G06F40/216 ,  G06N5/022

    Abstract: Aspects of the disclosure are directed to a Semi-supervised Pseudo-labeler Anomaly Detection with Ensembling (SPADE) framework that is not limited by the assumption that labeled and unlabeled data come from the same distribution. SPADE utilizes an ensemble of one-class classifiers as the pseudo-labeler to improve the robustness of pseudo-labeling with distribution mismatch. Partial matching automatically selects critical hyper-parameters for pseudo-labeling without validation data, which is crucial with a limited amount of labeled data.

    Self-supervised learning for anomaly detection and localization
    7.
    发明授权
    Self-supervised learning for anomaly detection and localization 有权

    公开(公告)号：US11941084B2

    公开(公告)日：2024-03-26

    申请号：US17454605

    申请日：2021-11-11

    Applicant: Google LLC

    Inventor： Kihyuk Sohn ,  Chun-Liang Li ,  Jinsung Yoon ,  Tomas Jon Pfister

    IPC: G06F18/214 ,  G06N3/08 ,  G06V10/22 ,  G06V10/774 ,  G06V10/82

    CPC classification number: G06F18/2155 ,  G06N3/08 ,  G06V10/22

    Abstract: A method for training a machine learning model includes obtaining a set of training samples. 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, cropping the training sample to generate a second cropped image that is different than the first cropped image, and duplicating a first portion of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion of the second cropped image to form an augmented second cropped image. 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.

    Self-Supervised Learning for Anomaly Detection and Localization
    8.
    发明申请
    Self-Supervised Learning for Anomaly Detection and Localization 有权

    公开(公告)号：US20220156521A1

    公开(公告)日：2022-05-19

    申请号：US17454605

    申请日：2021-11-11

    Applicant: Google LLC

    Inventor： Kihyuk Sohn ,  Chun-Liang Li ,  Jinsung Yoon ,  Tomas Jon Pfister

    IPC: G06K9/62 ,  G06K9/20 ,  G06N3/08

    Abstract: A method for training a machine learning model includes obtaining a set of training samples. 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, cropping the training sample to generate a second cropped image that is different than the first cropped image, and duplicating a first portion of the second cropped image. The method also includes overlaying the duplicated first portion of the second cropped image on a second portion of the second cropped image to form an augmented second cropped image. 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.

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