Method for generating anomalous data
AssigneeMakinaRocks Co., Ltd.
InventorsKi Hyun KIM, Hooncheol SHIN
Life of this patent
Estimating the term…
Abstract
Disclosed is a non-transitory computer readable medium storing a computer program. When the computer program is executed by one or more processors of a computer device, the computer program causes one or more processes to perform the following operations for data processing, and the operations may include: calculating a first probability distribution and a first sample statistical amount for a first sample data set; training a pseudo anomaly generation model that learns a second probability distribution and a second sample statistical amount for a second sample data set; and generating a training data set based on the pseudo anomaly generation model.
Claims16
A non-transitory computer readable medium storing a computer program, wherein the computer program comprises instructions for causing one or more processors of a computing device to perform a method for data processing, the method comprising:
- calculating a first probability distribution and a first sample statistical amount for a first sample data set by using the first sample data set;
- training a pseudo anomaly generation model that learns a second probability distribution and a second sample statistical amount for a second sample data set;
- and
- generating a training data set based on the pseudo anomaly generation model.
A computing device for data processing, comprising:
- a processor;
- and
- a memory,
- wherein the processor is configured to:
- calculate a first probability distribution and a first sample statistical amount for first sample data,
- train a pseudo anomaly generation model that learns a second probability distribution and a second sample statistical amount for a second sample data set, and
- generate a training data set based on the pseudo anomaly generation model.
A method for generating pseudo anomaly performed by one or more processors of a computer device, the method comprising:
- calculating a first probability distribution and a first sample statistical amount for a first sample data set by using the first sample data set;
- training a pseudo anomaly generation model that learns a second probability distribution and a second sample statistical amount for a second sample data set;
- and
- generating a training data set based on the pseudo anomaly generation model.
Dependencies are inferred from the claim text.