Machine Learning Model Training With Domain-Adapted Pseudo-Labels
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Developing machine learning models is an expensive and time-consuming process due to the difficulty in obtaining labeled training data, and accurately training these models is challenging without sufficient labeled data.
Innovation Solution
A method involving a first machine learning model and a domain adaptation model is used to generate pseudo labels for unlabeled data, which are then updated through iterative training with a second machine learning model, ultimately leading to labeled data for training a third model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional labeled training data is used to train machine learning models, then model accuracy is improved, but development cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary labeling actions by generating pseudo-labels for unlabeled data using existing labeled data and machine learning models. This preliminary labeling enables subsequent training to proceed faster without waiting for manual annotation of all training data, thereby reducing development time while maintaining model accuracy through iterative refinement of labels.
2Measurement precision
If more labeled training data is obtained to improve model accuracy, then model performance is enhanced, but data acquisition cost and complexity increase
Solution Approach 1:
The system enables self-service labeling by allowing machine learning models to generate their own training labels through pseudo-labeling mechanisms. The models use existing labeled data to automatically label unlabeled data, eliminating the need for external manual annotation services and reducing the complexity of data acquisition while providing sufficient labeled data for accurate model training.
3Measurement precision
If manual data labeling is performed to ensure data quality, then labeling accuracy is improved, but processing speed and productivity decrease
Solution Approach 1:
The system implements feedback mechanisms where machine learning models generate pseudo-labels that are then used to retrain and improve the models. This iterative feedback loop allows the system to automatically refine labeling accuracy over time without manual intervention, maintaining high labeling quality while achieving rapid data processing speeds that manual labeling cannot match.
Data Source
AI summary
A method of labeling data and training a model is provided. The method includes obtaining a set of images. The set of images includes a first subset and a second subset. The first subset is associated with a first set of labels. The method also includes generating a set of pseudo labels for the set of images and a second set of labels for the second subset based on the first subset, the second subset, a first machine learning model, and a domain adaption model. The method further includes generating second machine learning model. The second machine learning model is generated based on the set of images, the set of pseudo labels, the first set of labels, and the second set of labels. The second set of labels is updated based on one or more inferences generated by the second machine learning model.