17949504. SYSTEMS AND METHODS FOR MULTI-TEACHER GROUP-DISTILLATION FOR LONG-TAIL CLASSIFICATION simplified abstract (Robert Bosch GmbH)
Contents
- 1 SYSTEMS AND METHODS FOR MULTI-TEACHER GROUP-DISTILLATION FOR LONG-TAIL CLASSIFICATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR MULTI-TEACHER GROUP-DISTILLATION FOR LONG-TAIL CLASSIFICATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR MULTI-TEACHER GROUP-DISTILLATION FOR LONG-TAIL CLASSIFICATION
Organization Name
Inventor(s)
Tanvir Mahmud of Austin TX (US)
Chun-Hao Liu of Fremont CA (US)
Burhaneddin Yaman of San Jose CA (US)
SYSTEMS AND METHODS FOR MULTI-TEACHER GROUP-DISTILLATION FOR LONG-TAIL CLASSIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17949504 titled 'SYSTEMS AND METHODS FOR MULTI-TEACHER GROUP-DISTILLATION FOR LONG-TAIL CLASSIFICATION
Simplified Explanation
The patent application describes methods and systems for classifying a long-tail distribution of data by using a feature-extractor backbone model and a classifier model to extract features and classify the data, respectively. The data is grouped into classes, each assigned to a teacher model for training. The outputs of the teacher models are merged into a final class prediction model for data classification.
- Feature-extractor backbone model extracts features from data
- Classifier model classifies data based on extracted features
- Data is grouped into classes and assigned to teacher models for training
- Outputs of teacher models are merged into a final class prediction model for data classification
Potential Applications
This technology could be applied in various fields such as:
- Image recognition
- Speech recognition
- Anomaly detection
- Predictive maintenance
Problems Solved
This technology helps in:
- Efficiently classifying data with long-tail distributions
- Improving accuracy of data classification
- Handling large volumes of data effectively
Benefits
The benefits of this technology include:
- Enhanced data classification performance
- Scalability for handling large datasets
- Improved accuracy in classifying diverse data
Potential Commercial Applications
This technology could be commercially applied in:
- Healthcare for medical image analysis
- Finance for fraud detection
- Manufacturing for quality control
- Retail for customer behavior analysis
Possible Prior Art
One possible prior art for this technology could be the use of ensemble learning methods in data classification, where multiple models are combined to improve prediction accuracy.
Unanswered Questions
How does this technology compare to existing methods for data classification?
This article does not provide a direct comparison with existing methods for data classification.
What are the limitations of this technology in handling extremely large datasets?
The article does not address the limitations of this technology in handling extremely large datasets.
Original Abstract Submitted
Methods and systems for classifying a long-tail distribution of data. Data deriving from one or more sensors is classified into a plurality of classes by using (i) a feature-extractor backbone model configured to extract features from the data, and (ii) a classifier model configured to classify the data based on the extracted features. The plurality of classes are grouped, with each group assigned to a respective teacher model. Each respective teacher model is trained with the data in its respective group, as well as the feature-extractor backbone model. The outputs of the teacher models are then merged into a final class prediction model configured to classify the data.
