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    Image enhancement via iterative refinement based on machine learning models
    1.
    发明授权
    Image enhancement via iterative refinement based on machine learning models 有权

    公开(公告)号：US12165289B2

    公开(公告)日：2024-12-10

    申请号：US18227120

    申请日：2023-07-27

    Applicant: Google LLC

    Inventor： Chitwan Saharia ,  Jonathan Ho ,  William Chan ,  Tim Salimans ,  David Fleet ,  Mohammad Norouzi

    IPC: G06T5/70 ,  G06N3/045 ,  G06N3/08 ,  G06T3/4007 ,  G06T5/50

    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.

    Image Enhancement via Iterative Refinement based on Machine Learning Models
    3.
    发明申请
    Image Enhancement via Iterative Refinement based on Machine Learning Models 有权

    公开(公告)号：US20250061551A1

    公开(公告)日：2025-02-20

    申请号：US18939994

    申请日：2024-11-07

    Applicant: Google LLC

    Inventor： Chitwan Saharia ,  Jonathan Ho ,  William Chan ,  Tim Salimans ,  David Fleet ,  Mohammad Norouzi

    IPC: G06T5/70 ,  G06N3/045 ,  G06N3/08 ,  G06T3/4007 ,  G06T5/50

    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.

    SEQUENCE MODELING USING IMPUTATION
    5.
    发明申请
    SEQUENCE MODELING USING IMPUTATION 有权

    公开(公告)号：US20230075716A1

    公开(公告)日：2023-03-09

    申请号：US17797872

    申请日：2021-02-08

    Applicant: Google LLC

    Inventor： William Chan ,  Chitwan Saharia ,  Geoffrey E. Hinton ,  Mohammad Norouzi ,  Navdeep Jaitly

    IPC: G06F40/47 ,  G06F40/284

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sequence modeling. One of the methods includes receiving an input sequence having a plurality of input positions; determining a plurality of blocks of consecutive input positions; processing the input sequence using a neural network to generate a latent alignment, comprising, at each of a plurality of input time steps: receiving a partial latent alignment from a previous input time step; selecting an input position in each block, wherein the token at the selected input position of the partial latent alignment in each block is a mask token; and processing the partial latent alignment and the input sequence using the neural network to generate a new latent alignment, wherein the new latent alignment comprises, at the selected input position in each block, an output token or a blank token; and generating, using the latent alignment, an output sequence.

    Sequence modeling using imputation
    10.
    发明授权
    Sequence modeling using imputation 有权

    公开(公告)号：US12242818B2

    公开(公告)日：2025-03-04

    申请号：US17797872

    申请日：2021-02-08

    Applicant: Google LLC

    Inventor： William Chan ,  Chitwan Saharia ,  Geoffrey E. Hinton ,  Mohammad Norouzi ,  Navdeep Jaitly

    IPC: G06F40/47 ,  G06F40/284

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sequence modeling. One of the methods includes receiving an input sequence having a plurality of input positions; determining a plurality of blocks of consecutive input positions; processing the input sequence using a neural network to generate a latent alignment, comprising, at each of a plurality of input time steps: receiving a partial latent alignment from a previous input time step; selecting an input position in each block, wherein the token at the selected input position of the partial latent alignment in each block is a mask token; and processing the partial latent alignment and the input sequence using the neural network to generate a new latent alignment, wherein the new latent alignment comprises, at the selected input position in each block, an output token or a blank token; and generating, using the latent alignment, an output sequence.

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