中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

123 records (took 0.025 seconds)

    PERSONALIZED FORM ERROR CORRECTION PROPAGATION
    2.
    发明公开
    PERSONALIZED FORM ERROR CORRECTION PROPAGATION 审中-公开

    公开(公告)号：US20240362941A1

    公开(公告)日：2024-10-31

    申请号：US18140143

    申请日：2023-04-27

    Applicant: Adobe Inc.

    Inventor： Silky Singh ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain ,  Abhinav Java

    IPC: G06V30/262 ,  G06V30/14 ,  G06V30/19 ,  G06V30/414

    CPC classification number: G06V30/274 ,  G06V30/1444 ,  G06V30/19147 ,  G06V30/414

    Abstract: A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.

    SYSTEMS AND METHODS FOR GENERATING SYNTHETIC TABULAR DATA FOR MACHINE LEARNING AND OTHER APPLICATIONS
    4.
    发明公开
    SYSTEMS AND METHODS FOR GENERATING SYNTHETIC TABULAR DATA FOR MACHINE LEARNING AND OTHER APPLICATIONS 审中-公开

    公开(公告)号：US20240330682A1

    公开(公告)日：2024-10-03

    申请号：US18295094

    申请日：2023-04-03

    Applicant: Adobe Inc.

    Inventor： Surgan JANDIAL ,  Siddarth Ramesh ,  Piyush Gupta ,  Gauri Gupta ,  Balaji Krishnamurthy

    IPC: G06N3/08 ,  G06N3/0455

    CPC classification number: G06N3/08 ,  G06N3/0455

    Abstract: Systems and methods for generating synthetic tabular data for machine learning and other applications are provided. In some embodiments, a variational autoencoder is trained to learn inter-feature correlations found in tabular data collected from real data sources. The trained variational autoencoder is used to train a generator model of a Generative Adversarial Network (GAN) to generate synthetic tabular data that exhibits the inter-feature correlation distribution found in the tabular data collected from real data sources. In some embodiments, processing devices perform operations comprising: receiving a set of tabular data records, each record comprising a plurality of features; training a first machine learning model using the tabular data records to learn correlations between the plurality of features; and training a second machine learning model, using the first machine learning model, to generate a synthetic tabular data records based at least on the one or more correlations between the plurality of features.

    FORM STRUCTURE SIMILARITY DETECTION
    5.
    发明公开
    FORM STRUCTURE SIMILARITY DETECTION 审中-公开

    公开(公告)号：US20240330351A1

    公开(公告)日：2024-10-03

    申请号：US18190686

    申请日：2023-03-27

    Applicant: Adobe Inc.

    Inventor： Abhinav Java ,  Surgan Jandial ,  Shripad Vilasrao Deshmukh ,  Milan Aggarwal ,  Mausoom Sarkar ,  Balaji Krishnamurthy ,  Arneh Jain

    IPC: G06F16/383 ,  G06F16/332 ,  G06V30/19 ,  G06V30/412

    CPC classification number: G06F16/383 ,  G06F16/332 ,  G06V30/19147 ,  G06V30/412

    Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

    Systems and methods of training neural networks against adversarial attacks
    9.
    发明授权
    Systems and methods of training neural networks against adversarial attacks 有权

    公开(公告)号：US11468314B1

    公开(公告)日：2022-10-11

    申请号：US16129553

    申请日：2018-09-12

    Applicant: ADOBE INC.

    Inventor： Mayank Singh ,  Abhishek Sinha ,  Balaji Krishnamurthy

    IPC: G06N3/08 ,  G06K9/62 ,  G06N3/04 ,  G06F21/57 ,  G06N5/04

    Abstract: Embodiments disclosed herein describe systems, methods, and products that generate trained neural networks that are robust against adversarial attacks. During a training phase, an illustrative computer may iteratively optimize a loss function that may include a penalty for ill-conditioned weight matrices in addition to a penalty for classification errors. Therefore, after the training phase, the trained neural network may include one or more well-conditioned weight matrices. The one or more well-conditioned weight matrices may minimize the effect of perturbations within an adversarial input thereby increasing the accuracy of classification of the adversarial input. By contrast, conventional training approaches may merely reduce the classification errors using backpropagation, and, as a result, any perturbation in an input is prone to generate a large effect on the output.

    IDENTIFYING DIGITAL ATTRIBUTES FROM MULTIPLE ATTRIBUTE GROUPS UTILIZING A DEEP COGNITIVE ATTRIBUTION NEURAL NETWORK
    10.
    发明申请
    IDENTIFYING DIGITAL ATTRIBUTES FROM MULTIPLE ATTRIBUTE GROUPS UTILIZING A DEEP COGNITIVE ATTRIBUTION NEURAL NETWORK 有权

    公开(公告)号：US20220309093A1

    公开(公告)日：2022-09-29

    申请号：US17806922

    申请日：2022-06-14

    Applicant: Adobe Inc.

    Inventor： Ayush Chopra ,  Mausoom Sarkar ,  Jonas Dahl ,  Hiresh Gupta ,  Balaji Krishnamurthy ,  Abhishek Sinha

    IPC: G06F16/535 ,  G06K9/62 ,  G06F17/15 ,  G06N3/04 ,  G06F16/55

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating tags for an object portrayed in a digital image based on predicted attributes of the object. For example, the disclosed systems can utilize interleaved neural network layers of alternating inception layers and dilated convolution layers to generate a localization feature vector. Based on the localization feature vector, the disclosed systems can generate attribute localization feature embeddings, for example, using some pooling layer such as a global average pooling layer. The disclosed systems can then apply the attribute localization feature embeddings to corresponding attribute group classifiers to generate tags based on predicted attributes. In particular, attribute group classifiers can predict attributes as associated with a query image (e.g., based on a scoring comparison with other potential attributes of an attribute group). Based on the generated tags, the disclosed systems can respond to tag queries and search queries.

Patent Agency Ranking