Image generation using adversarial attacks for imbalanced datasets
AssigneeMicrosoft Technology Licensing, LLC
InventorsGaurav Mittal, Nikolaos Karianakis, Victor Manuel Fragoso Rojas, Mei Chen, Jedrzej Jakub Kozerawski
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Abstract
A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.
Claims17
A method of balancing a dataset for a machine learning model, the method comprising:
- receiving a machine learning model implemented in a computing system;
- identifying confusing classes of few-shot classes for the machine learning model during validation;
- selecting one of the confusing classes;
- selecting an image from one of the few-shot classes;
- computing an image perturbation such that the selected image is classified as the selected confusing class, wherein the image perturbation is computed using a gradient-ascent technique that propagates a gradient to an input image;
- modifying the selected image with the computed perturbation;
- and
- adding the modified selected image to the one few-shot class for training the machine learning model.
A computing system, comprising:
- one or more processors;
- and
- a computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by the processor, cause the computing system to perform operations comprising:
- selecting a confusing class of few-shot classes for a machine learning model;
- selecting an image from one of the few-shot classes;
- computing an image perturbation such that the selected image is classified as the selected confusing class, wherein the image perturbation is computed using a gradient-ascent technique that propagates a gradient to an input image;
- modifying the selected image with the computed perturbation;
- and
- adding the modified selected image to a batch for training the machine learning model.
A computer-readable storage medium having computer-executable instructions stored thereupon which, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising:
- receiving a machine learning model implemented in a computing system;
- identifying confusing classes of few-shot classes for the machine learning model during validation;
- selecting one of the confusing classes;
- selecting an image from one of the few-shot classes;
- computing an image perturbation such that the selected image is classified as the selected confusing class, wherein the image perturbation is computed using a gradient-ascent technique that propagates a gradient to an input image;
- modify the selected image with the computed perturbation;
- and
- adding the modified selected image to a batch for training the machine learning model.
Dependencies are inferred from the claim text.