ML model drift detection using modified GAN
AssigneeRaytheon Company
InventorNicole M. Hatten
Life of this patent
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Abstract
Discussed herein are devices, systems, and methods for machine learning (ML) model drift detection. A method can include receiving machine learning (ML) data defining a number of layers of neurons, a number of neurons per each layer, and weights for each neuron of a deployed ML model, operating the deployed ML model in a modified generative adversarial network (GAN) architecture, while operating the deployed ML model, recording output of a hidden layer of the deployed ML model, determining a metric of the output, and re-deploying the deployed ML model and monitoring whether the re-deployed ML model is suffering from ML model drift based on the metric.
Claims15
A device comprising:
- at least one memory including instructions stored thereon;
- and
- processing circuitry configured to execute the instructions, the instructions, when executed, cause the processing circuitry to perform operations comprising:
- receiving machine learning (ML) data defining a number of layers of neurons, a number of neurons per each layer, and weights for each neuron of a deployed ML model;
- operating the deployed ML model in a modified generative adversarial network (GAN) architecture, the modified GAN architecture includes fake data, from a generator of an unmodified GAN architecture that includes a discriminator, input to the deployed ML model, the modified GAN architecture includes no feedback loops configured to update weights of neurons of the modified GAN architecture;
- while operating the deployed ML model, recording output of a hidden layer of the deployed ML model;
- determining a metric of the output;
- and
- re-deploying the ML model and monitoring whether the re-deployed ML model is suffering from ML model drift based on the metric.
A non-transitory machine-readable medium including instructions that, when executed by a machine, cause the machine to perform operations comprising:
- receiving machine learning (ML) data defining a number of layers of neurons, a number of neurons per each layer, and weights for each neuron of a deployed ML model;
- operating the deployed ML model in a modified generative adversarial network (GAN) architecture, the modified GAN architecture includes fake data, from a generator of an unmodified GAN architecture that includes a discriminator, input to the deployed ML model, the modified GAN architecture includes no feedback loops configured to update weights of neurons of the modified GAN architecture;
- while operating the deployed ML model, recording output of a hidden layer of the deployed ML model;
- determining a metric of the output;
- and
- re-deploying the deployed ML model and monitoring whether the re-deployed ML model is suffering from ML model drift based on the metric.
A method comprising:
- receiving machine learning (ML) data defining a number of layers of neurons, a number of neurons per each layer, and weights for each neuron of a deployed ML model;
- operating the deployed ML model in a modified generative adversarial network (GAN) architecture, the modified GAN architecture includes fake data, from a generator of an unmodified GAN architecture that includes a discriminator, input to the deployed ML model, the modified GAN architecture includes no feedback loops configured to update weights of neurons of the modified GAN architecture;
- while operating the deployed ML model, recording output of a hidden layer of the deployed ML model;
- determining a metric of the output;
- and
- re-deploying the deployed ML model and monitoring whether the re-deployed ML model is suffering from ML model drift based on the metric.
Dependencies are inferred from the claim text.