US 2025/0156752 A1 · DATA AUGMENTATION USING DIFFERENT IN-DOMAIN DATA

Abstract

A method, computer system, and a computer program product are provided for data augmentation for training an artificial intelligence (AI) engine. The technique comprises encoding an in-distribution dataset having a plurality of components and an out-of-distribution dataset also having a plurality of components. The encoding is performed using a foundation model. The techniques also comprises pairing one in-distribution component from the dataset with an out-of-distribution component from the dataset in a same class to provide a first set of paired component and pairing another in-distribution component with another out-of-distribution component in a different class using the contrastive learning model to provide a second set of paired components. The first and second set of pared components are then augmented to generate an augmented training dataset. The foundation models is adjusted by using he augmented training dataset to train the AI engine.

Claims20

Dependent claims

A method for data augmentation for training an artificial intelligence (AI) engine, comprising:

encoding an in-distribution dataset having a plurality of in-distribution components, and an out-of-distribution dataset having a plurality of in-distribution components, wherein said encoding is done using a foundation model;
pairing an in-distribution component with an out-of-distribution component in a same class and using a contrastive model to provide a first set of paired components;
pairing another in-distribution component with another out-of-distribution component in a different class using said contrastive learning model to provide a second set of paired components;
augmenting said first and second set of paired components to generate an augmented training dataset;
and
adjusting said foundation model by using said augmented training dataset to train said AI engine.

3 more elements

A computer system for data augmentation for training an artificial intelligence (AI) engine, comprising:

one or more processors, one or more computer-readable memories and one or more computer-readable storage media;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to encode an in-distribution dataset and an out-of-distribution dataset using a foundation model;
wherein said encoding is done using a foundation model;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to pair an in-distribution component with an out-of-distribution component in a same class using a contrastive model to provide a first set of paired components;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to pair another in-distribution component with another out-of-distribution component in a different class using said contrastive learning model to provide a second set of paired components;
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to augment said first and second set of paired components to generate an augmented training dataset;
and
program instructions, stored on at least one of the one or more storage media for execution by at least one of the one or more processors via at least one of the one or more memories, to adjust said foundation model by using said augmented training dataset to train said AI engine.

5 more elements

Dependencies are inferred from the claim text.

Data augmentation using different in-domain data

Life of this patent

Data augmentation using different in-domain data

Life of this patent

Abstract

Claims20