MACHINE-LEARNING TECHNIQUES FOR REPRESENTING ITEMS IN A SPECTRAL DOMAIN

    公开(公告)号:US20230267306A1

    公开(公告)日:2023-08-24

    申请号:US17933806

    申请日:2022-09-20

    CPC classification number: G06N3/0454 G06T5/10 G06T2207/20056

    Abstract: In various embodiments, a training application generates a trained machine learning model that represents items in a spectral domain. The training application executes a first neural network on a first set of data points associated with both a first item and the spectral domain to generate a second neural network. Subsequently, the training application generates a set of predicted data points that are associated with both the first item and the spectral domain via the second neural network. The training application generates the trained machine learning model based on the first neural network, the second neural network, and the set of predicted data points. The trained machine learning model maps one or more positions within the spectral domain to one or more values associated with an item based on a set of data points associated with both the item and the spectral domain.

    MACHINE-LEARNING TECHNIQUES FOR SPARSE-TO-DENSE SPECTRAL RECONSTRUCTION

    公开(公告)号:US20230267659A1

    公开(公告)日:2023-08-24

    申请号:US17933811

    申请日:2022-09-20

    CPC classification number: G06T11/006 G06F17/141 G01B9/02041

    Abstract: In various embodiments, an inference application reconstructs representations of items in a spectral domain. The inference application maps a first set of data points associated with a both an item and the spectral domain to conditioning information via a first trained machine learning model. The inference application updates a second trained machine learning model based on the conditioning information to generate a model that represents the item within the spectral domain. The inference application generates a second set of data points associated with both the item and the spectral domain via the model. The inference application constructs an image associated with the item based on the second set of data points.

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