US 12,223,422 B2Granted

Continuous training methods for systems identifying anomalies in an image of an object

AssigneeServiceNow Canada Inc.

InventorNegin Sokhandan Asl

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Abstract

A system identifying anomalies in an image of an object is first trained using first sets of images corresponding to first anomaly types for the object. A model of the object is formed in a latent space. A label for each anomalous image is used to calculate vectors containing means and standard deviations for each first anomaly types. The means and standard deviations are used to calculate a log-likelihood loss for each first anomaly type. The system is retrained using second sets of images corresponding to second anomaly types for the object. The vectors are supplemented using labels for each second anomaly types. A statistically sufficient sample of information in the means and standard deviations vectors is supplied to the latent space. A log-likelihood loss for each of the first and second anomaly types is calculated based on their respective mean and standard deviation.

Claims17

Dependent claims

A computer-implemented continuous training method for a system identifying anomalies in an image of an object, comprising:

training the system by:
supplying, to an image encoder, one or more first sets of images corresponding to one or more first anomaly types for the object, the image encoder forming a model of the object in a latent space,
supplying labels to an anomaly encoder, each label corresponding to a respective image among the one or more first sets of images corresponding to the one or more first anomaly types for the object, each label identifying a related anomaly type for the object,
calculating, at the anomaly encoder, a vector containing a mean for each of one or more first model modes defined for the one or more first anomaly types,
calculating, at the anomaly encoder, a vector containing a standard deviation for each of the one or more first model modes defined for the one or more first anomaly types, and
calculating a log-likelihood loss for each of the one or more first anomaly types based on their respective mean and standard deviation;
and
retraining the system by:
supplying, to the image encoder, one or more second sets of images corresponding to one or more second anomaly types for the object, the image encoder updating the model of the object in the latent space,
supplying additional labels, to the anomaly encoder, each additional label corresponding to a respective image among the one or more second sets of images corresponding to the one or more second anomaly types for the object, each additional label identifying a related anomaly type for the object,
updating, at the anomaly encoder, the vector containing the mean for each of the one or more first model modes defined for the one or more first anomaly types by adding a mean for each of one or more second model modes defined for the one or more second anomaly types,
updating, at the anomaly encoder, the vector containing the standard deviation for each of the one or more first model modes defined for the one or more first anomaly types by adding a standard deviation for each of one or more second model modes defined for the one or more second anomaly types,
supplying, to the latent space, a statistically sufficient sample of information contained in the vectors containing the means and standard deviations, and
calculating a log-likelihood loss for each of the first and second anomaly types based on their respective mean and standard deviation.

11 more elements

A system for identifying anomalies in an object, comprising:

an image encoder;
an anomaly encoder;
and
a training engine adapted to train the system by:
supplying, to the image encoder, one or more first sets of images corresponding to one or more first anomaly types for the object, the image encoder forming a model of the object in a latent space,
supplying labels to the anomaly encoder, each label corresponding to a respective image among the one or more first sets of images corresponding to the one or more first anomaly types for the object, each label identifying a related anomaly type for the object,
calculating, at the anomaly encoder, a vector containing a mean for each of one or more first model modes defined for the one or more first anomaly types,
calculating, at the anomaly encoder, a vector containing a standard deviation for each of the one or more first model modes defined for the one or more first anomaly types, and
calculating a log-likelihood loss for each of the one or more first anomaly types based on their respective mean and standard deviation;
and
the training engine being also adapted to retrain the system by:
supplying, to the image encoder, one or more second sets of images corresponding to one or more second anomaly types for the object, the image encoder updating the model of the object in the latent space,
supplying additional labels to the anomaly encoder, each additional label corresponding to a respective image among the one or more second sets of images corresponding to the one or more second anomaly types for the object, each label identifying a related anomaly type for the object,
updating, at the anomaly encoder, the vector containing the mean for each of the one or more first model modes defined for the one or more first anomaly types by adding a mean for each of one or more second model modes defined for the one or more second anomaly types,
updating, at the anomaly encoder, the vector containing the standard deviation for each of the one or more first model modes defined for the one or more first anomaly types by adding a standard deviation for each of one or more second model modes defined for the one or more second anomaly types,
supplying, to the latent space, a statistically sufficient sample of information contained in the vectors containing the means and standard deviations, and
calculating a log-likelihood loss for each of the first and second anomaly types based on their respective mean and standard deviation.

14 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/062,019Filed 2 Oct 2020
Published: US 2022/0108163 A17 Apr 2022
Granted: 11 Feb 2025
Term ends: Not estimatedThe source records don’t support an estimate.

Classification15 codes

G06N 3/08
Neural networks: learning methods
G06N 3/088
Learning methods: non-supervised learning, e.g. competitive learning
G06N 3/045
Architecture, e.g. interconnection topology: combinations of networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks

Family

US 2022/0108163 A1Pre-grant publication · published 7 Apr 2022
US 12,223,422 B2This patent

From the US filing history. Foreign family members aren’t listed.

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