Active Learning Sampling Logic for AI Dataset Labeling
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Solution Overview
Problem
Deep learning models require massive labeled data for training, which is labor-intensive and time-consuming to annotate, especially for new tasks, leading to a bottleneck in model development and performance improvement.
Innovation Solution
The implementation of a human-in-the-loop (HITL) active learning system that uses real-time human feedback to optimize future sampling priority, allowing users to communicate with the model and sampling logic to select the most informative instances for annotation, thereby accelerating model convergence and improving performance with minimal queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If massive labeled data is collected for deep learning training, then model performance is improved, but annotation time and labor cost increase significantly
Solution Approach 1:
The patent implements a feedback loop where human annotators provide corrections to model predictions, and these corrections are used to update the model. The system continuously learns from human feedback, improving prediction accuracy over time while reducing the need for extensive manual annotation of all training data.
Solution Approach 2:
The system enables the model to perform automatic annotation of training data, serving itself by generating initial labels that human annotators only need to verify or correct. This self-annotation capability significantly reduces the time and labor required for data preparation while maintaining model performance.
2Measurement precision
If all samples are annotated for new tasks, then model accuracy is improved, but annotation cost and time increase exponentially
Solution Approach 1:
The patent applies partial action by annotating only a subset of samples rather than all samples. The system identifies and annotates only the most informative or uncertain samples, achieving good model accuracy with significantly reduced annotation effort compared to annotating the entire dataset.
Solution Approach 2:
The system dynamically adjusts the sampling strategy based on model confidence levels and uncertainty metrics. By changing the parameters of sample selection (focusing on high-uncertainty or high-impact samples), the system achieves efficient annotation that maximizes model accuracy improvement per annotation effort.
3Productivity
If synthetic data is generated to speed up model iteration, then data annotation cost is reduced, but data quality and model precision decrease
Solution Approach 1:
The patent merges synthetic data and real annotated data into a combined training dataset. The system uses synthetic data for initial model training and iteration speedup, then incorporates real annotated data (especially human-corrected samples) to refine and improve model precision, achieving both fast iteration and high accuracy.
Solution Approach 2:
Human annotators serve as an intermediary between synthetic data and final model training. The system uses human corrections to synthetic data predictions to create improved training samples, acting as a mediator that enhances synthetic data quality while maintaining the benefits of fast synthetic data generation.
4Productivity
If human-in-the-loop active learning is implemented, then annotation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements a unified system that combines active learning, model training, and human feedback processing into a single multi-functional framework. The same core infrastructure handles multiple tasks including sample selection, model prediction, human interface management, and training data preparation, reducing overall system complexity despite the advanced capabilities.
Data Source
AI summary
The technology disclosed enables a user to optimize a sampling logic to increase the future sampling likelihood of those instances that are similar to the instances that the user believes are informative, and decrease the future sampling likelihood of those instances that are similar to the instances that the user believes are non-informative.