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US 11,531,875 B2Granted
Systems and methods for generating datasets for model retraining
AssigneeNasdaq, Inc.
InventorsAnand Dwivedi, Hyunsoo Jeong
Life of this patent
Estimating the term…
Abstract
A computer system is provided and programmed to assemble a plurality of synthetic datasets and blend those synthetic datasets into a synthesized dataset. An evaluation is then performed to determine whether an existing model should be associated with the synthesized dataset or a new model should be trained from an existing model using the synthesized dataset.
Claims20
A computer system comprising:
- non-transitory computer readable memory that is configured to store:
- a reference model;
- and
- a reference dataset that is associated with the reference model;
- a processing system that includes at least one hardware processor, the processing system configured to:
- generate a plurality of synthetic datasets that are derived from labeled detection frames;
- generate, for each synthetic dataset of the plurality of synthetic datasets, a plurality of feature metrics for a plurality of features from the each synthetic dataset, wherein the feature metrics are generated based on the reference dataset;
- use a first neural network to generate, based on the determined plurality of feature metrics, a dataset similarity score for each of the plurality of synthetic datasets with respect to the reference dataset, wherein each of the dataset similarity scores indicates how similar a given synthetic dataset is to the reference dataset;
- generate, for each of the plurality of synthetic datasets, a training similarity score by training a neural network architecture of the reference model by using a corresponding synthetic dataset;
- and
- generate a synthesized dataset by combining data from the plurality of synthetic datasets based on the training similarity scores and the dataset similarity scores.
8 more elements
A method implemented on a computer system, the method comprising:
- storing, to a non-transitory storage medium, a reference model and a reference dataset that is associated with the reference model;
- generating a plurality of synthetic datasets that are derived from labeled detection frames;
- generating, for each synthetic dataset of the plurality of synthetic datasets, a plurality of feature metrics for a plurality of features from the synthetic dataset, wherein the feature metrics are generated based on comparison to the reference dataset;
- using a first neural network to generate, based on the determined plurality of feature metrics, a dataset similarity score for each of the plurality of synthetic datasets with respect to the reference dataset, wherein each of the dataset similarity scores indicates how similar a given synthetic dataset is to the reference dataset;
- generating, for each of the plurality of synthetic datasets, a training similarity score by training a neural network architecture of the reference model by using a corresponding synthetic dataset;
- and
- constructing a synthesized dataset by combining data from the plurality of synthetic datasets based on the training similarity scores and the dataset similarity scores.
4 more elements
A non-transitory computer readable storage medium configured to store computer-executable instructions for use with a computer system, the stored computer-executable instructions comprising instructions that cause the computer system to perform operations comprising:
- storing, to a non-transitory storage medium, a reference model and a reference dataset that is associated with the reference model;
- generating a plurality of synthetic datasets that are derived from labeled detection frames;
- generating, for each synthetic dataset of the plurality of synthetic datasets, a plurality of feature metrics for a plurality of features from the synthetic dataset, wherein the feature metrics are generated based on comparison to the reference dataset;
- using a first neural network to generate, based on the determined plurality of feature metrics, a dataset similarity score for each of the plurality of synthetic datasets with respect to the reference dataset, wherein each of the dataset similarity scores indicates how similar a given synthetic dataset is to the reference dataset;
- generating, for each of the plurality of synthetic datasets, a training similarity score by training a neural network architecture of the reference model by using a corresponding synthetic dataset;
- and
- constructing a synthesized dataset by combining data from the plurality of synthetic datasets based on the training similarity scores and the dataset similarity scores.
4 more elements
Dependencies are inferred from the claim text.