18351100. METHOD AND DEVICE WITH AUTOMATIC LABELING simplified abstract (Samsung Electronics Co., Ltd.)
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 Unanswered Questions
- 1.11 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 18351100 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 estimating correct labels for the data samples.
- The first model is trained to predict confidences of labels for data samples in a training dataset.
- A corrected data sample is 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.
- The second model is trained to estimate correct labels for the data samples.
- It estimates a correct other label corresponding to another incorrect label detected based on a corresponding confidence generated by the first model.
- The other incorrect label is automatically corrected with the estimated correct other label.
Potential Applications
This technology could be applied in various fields such as:
- Machine learning
- Data analysis
- Predictive modeling
Problems Solved
This technology helps in:
- Improving label prediction accuracy
- Correcting incorrect labels in datasets
- Enhancing the performance of predictive models
Benefits
The benefits of this technology include:
- Increased accuracy in label predictions
- Enhanced data quality
- Improved model performance
Potential Commercial Applications
This technology could be commercially applied in:
- Data analytics software
- Predictive modeling tools
- Machine learning platforms
Possible Prior Art
One possible prior art for this technology could be:
- Research papers on label correction in machine learning models
Unanswered Questions
How does this method handle noisy data in the training dataset?
The method does not explicitly mention how it deals with noisy data that may affect the training of models.
What is the computational complexity of training the first and second models?
The abstract does not provide information on the computational resources required for training the models.
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.
