Microsoft technology licensing, llc (20240211547). IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS simplified abstract
Contents
- 1 IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about the Technology
- 1.11 Original Abstract Submitted
IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS
Organization Name
microsoft technology licensing, llc
Inventor(s)
Gaurav Mittal of Redmond WA (US)
Nikolaos Karianakis of Sammamish WA (US)
Victor Manuel Fragoso Rojas of Bellevue WA (US)
Jedrzej Jakub Kozerawski of Goleta CA (US)
IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211547 titled 'IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS
Simplified Explanation
The patent application describes a method for balancing a dataset for a machine learning model by identifying confusing classes during validation and modifying images to improve model performance.
Key Features and Innovation
- Method for identifying confusing classes in few-shot classes during validation.
- Computation of image perturbations to modify images for training.
- Addition of modified images to the training batch to improve model performance.
Potential Applications
This technology can be applied in various machine learning tasks where dataset imbalance affects model performance, such as image classification, object detection, and natural language processing.
Problems Solved
This technology addresses the issue of imbalanced datasets in machine learning models, which can lead to biased predictions and reduced model accuracy.
Benefits
- Improved model performance by balancing dataset classes.
- Enhanced accuracy and generalization of machine learning models.
- Reduction of bias in predictions due to imbalanced datasets.
Commercial Applications
- This technology can be utilized in industries such as healthcare for medical image analysis, finance for fraud detection, and e-commerce for personalized recommendations. The market implications include more accurate predictions, improved customer satisfaction, and increased efficiency in decision-making processes.
Questions about the Technology
How does this method improve the performance of machine learning models?
This method improves model performance by balancing the dataset classes, which helps prevent bias and improve accuracy in predictions.
What are the potential applications of this technology beyond machine learning?
The technology can be applied in various fields such as healthcare, finance, and e-commerce for tasks like image analysis, fraud detection, and recommendation systems.
Original Abstract Submitted
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.
