Samsung electronics co., ltd. (20240161007). METHOD AND DEVICE WITH AUTOMATIC LABELING simplified abstract
Contents
- 1 METHOD AND DEVICE WITH AUTOMATIC LABELING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND DEVICE WITH AUTOMATIC LABELING - 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
METHOD AND DEVICE WITH AUTOMATIC LABELING
Organization Name
Inventor(s)
Hyun Sung Chang of Suwon-si (KR)
METHOD AND DEVICE WITH AUTOMATIC LABELING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161007 titled 'METHOD AND DEVICE WITH AUTOMATIC LABELING
Simplified Explanation
The abstract describes a method for training models to predict confidences of labels for data samples, correcting incorrect labels, and automatically correcting other incorrect labels with estimated correct labels.
- Training a first model to predict label confidences for data samples in a training dataset.
- Using a corrected data sample obtained by correcting an incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by a second model.
- Training the second model to estimate correct labels for the data samples.
- Estimating a correct other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model.
- Automatically correcting the other incorrect label with the estimated correct other label.
Potential Applications
This technology could be applied in various fields such as:
- Natural language processing
- Image recognition
- Fraud detection
Problems Solved
This technology helps in:
- Improving the accuracy of label predictions
- Correcting mislabeled data samples
- Enhancing the overall performance of machine learning models
Benefits
The benefits of this technology include:
- Increased efficiency in training models
- Enhanced data quality
- Improved decision-making based on more accurate predictions
Potential Commercial Applications
The technology could be utilized in industries such as:
- Healthcare for patient diagnosis
- Finance for fraud detection
- E-commerce for personalized recommendations
Possible Prior Art
One possible prior art could be the use of ensemble learning techniques to improve model predictions by combining multiple models' outputs.
What are the potential limitations of this technology?
This technology may have limitations in:
- Handling highly complex datasets
- Scalability for large-scale applications
How does this technology compare to existing methods for label correction in machine learning?
This technology stands out by:
- Automatically correcting incorrect labels based on confidence levels
- Training models to estimate correct labels for data samples
Original Abstract Submitted
a processor-implemented method includes training a first model to predict confidences of labels for data samples in a training dataset, including using a corrected data sample obtained by correcting an incorrect label based on a corresponding confidence detected by the first model and an estimated corrected label generated by a second model; training the second model to estimate correct labels for the data samples, including estimating a correct other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model with respect to the other incorrect label; and automatically correcting the other incorrect label with the estimated correct other label.
