US 12,223,422 B2
Continuous training methods for systems identifying anomalies in an image of an object
Negin Sokhandan Asl, Montreal (CA)
Assigned to SERVICENOW CANADA INC., Montreal (CA)
Filed by ELEMENT AI INC., Montreal (CA)
Filed on Oct. 2, 2020, as Appl. No. 17/062,019.
Prior Publication US 2022/0108163 A1, Apr. 7, 2022
Int. Cl. G06N 3/08 (2023.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06N 3/08 (2013.01) [G06V 10/7753 (2022.01); G06V 10/82 (2022.01)] 17 Claims
OG exemplary drawing
 
1. 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.