US 2019/0156200 A1 · SYSTEM AND METHOD FOR ANOMALY DETECTION VIA A MULTI-PREDICTION-MODEL ARCHITECTURE

Abstract

In some embodiments, anomaly detection may be facilitated via a multi-neural-network architecture. In some embodiments, a first neural network may be configured to generate hidden representations of data items corresponding to a concept. A second neural network may be configured to generate reconstructions of the data items from the hidden representations. The first neural network may be configured to assess the reconstructions against the data items and update configurations of the first neural network based on the assessment of the reconstructions. Subsequent to the update of the first neural network, the first neural network may generate a hidden representation of a first data item from the first data item. The second neural network may generate a reconstruction of the first data item from the hidden representation. An anomaly in the first data item may be detected based on differences between the first data item and the reconstruction.

Claims21

Dependent claims

1-20. (canceled)

A method of facilitating anomaly detection via a multi-model architecture, the method being implemented by one or more processors executing computer program instructions that, when executed, perform the method, the method comprising:

obtaining data items that corresponds to a concept;
providing the data items to a first model to cause the first model to generate hidden representations of the data items from the data items;
providing the hidden representations of the data items to a second model to cause the second model to generate reconstructions of the data items from the hidden representations of the data items;
providing the reconstructions of the data items to the first model, the first model updating one or more representation-generation-related configurations of the first model based on the data items and the reconstructions of the data items.

1 more element

A system comprising:

one or more processors executing computer program instructions that, when executed, cause the one or more processors to:
obtain data items that corresponds to a concept;
provide the data items to a first model to cause the first model to generate hidden representations of the data items from the data items;
provide the hidden representations of the data items to a second model to cause the second model to generate reconstructions of the data items from the hidden representations of the data items;
and
provide the reconstructions of the data items to the first model, the first model updating one or more representation-generation-related configurations of the first model based on the reconstructions of the data items.

3 more elements

A system comprising:

a first model configured to generate hidden representations of data items from the data items, the data items corresponding to a concept;
a second model configured to generate reconstructions of the data items from the hidden representations of the data items;
wherein the first model is configured to:
obtain the reconstructions of the data items;
and
update one or more representation-generation-related configurations of the first model based on the reconstructions of the data items.

3 more elements

Dependencies are inferred from the claim text.

System and method for anomaly detection via a multi-prediction-model architecture

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System and method for anomaly detection via a multi-prediction-model architecture

Life of this patent

Abstract

Claims21