18599029. IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS simplified abstract (Microsoft Technology Licensing, LLC)
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 18599029 titled 'IMAGE GENERATION USING ADVERSARIAL ATTACKS FOR IMBALANCED DATASETS
Simplified Explanation
This patent application describes a method for balancing a dataset for a machine learning model by identifying confusing classes, selecting an image from one of these classes, computing an image perturbation to modify the selected image, and adding it to the training batch.
Key Features and Innovation
- Method for balancing datasets in machine learning models.
- Identification of confusing classes during validation.
- Selection of images from few-shot classes.
- Computation of image perturbations to modify selected images.
- Addition of modified images to training batches.
Potential Applications
This technology can be applied in various fields such as:
- Image classification.
- Object detection.
- Natural language processing.
- Medical diagnosis.
- Autonomous vehicles.
Problems Solved
- Imbalance in datasets affecting machine learning model performance.
- Lack of representation for few-shot classes.
- Difficulty in training models with limited data.
Benefits
- Improved accuracy of machine learning models.
- Enhanced performance on underrepresented classes.
- Increased robustness and generalization of models.
- Efficient training process with balanced datasets.
Commercial Applications
- This technology can be utilized in industries such as:
- Healthcare for disease diagnosis.
- E-commerce for recommendation systems.
- Finance for fraud detection.
- Automotive for autonomous driving systems.
- Security for threat detection.
Questions about the Technology
How does this method improve the performance of machine learning models?
This method helps in balancing datasets, ensuring all classes have sufficient representation for better model training and accuracy.
What are the potential applications of this technology beyond machine learning?
Apart from machine learning, this technology can be applied in various fields such as image processing, data augmentation, and pattern recognition.
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.
