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    Machine Learning Model for Detecting Out-Of-Distribution Inputs
    1.
    发明公开
    Machine Learning Model for Detecting Out-Of-Distribution Inputs 审中-公开

    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.

    Systems And Methods For Performing Automatic Label Smoothing Of Augmented Training Data
    3.
    发明申请
    Systems And Methods For Performing Automatic Label Smoothing Of Augmented Training Data 有权

    公开(公告)号：US20220108220A1

    公开(公告)日：2022-04-07

    申请号：US17493228

    申请日：2021-10-04

    Applicant: Google LLC

    Inventor： Yao Qin ,  Alex Beutel ,  Ed Huai-Hsin Chi ,  Xuezhi Wang ,  Balaji Lakshminarayanan

    IPC: G06N20/00

    Abstract: Example aspects of the present disclosure are directed to systems and methods for performing automatic label smoothing of augmented training data. In particular, some example implementations of the present disclosure which in some instances can be referred to “AutoLabel” can automatically learn the labels for augmented data based on the distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. AutoLabel is a generic framework that can be easily applied to existing data augmentation methods, including AugMix, mixup, and adversarial training, among others. AutoLabel can further improve clean accuracy, as well as the accuracy and calibration over corrupted datasets. Additionally, AutoLabel can help adversarial training by bridging the gap between clean accuracy and adversarial robustness.

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