Active Curriculum Learning for Lower-Cost Data Annotation
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Solution Overview
Problem
Modern deep learning architectures require a large amount of labeled data to achieve high performance, and traditional random data sampling for labeling is costly and inefficient.
Innovation Solution
A method for training machine learning models using a combination of active learning (AL) and curriculum learning (CL) metrics to selectively label and order data based on difficulty scores, including linguistic features and model uncertainty, to reduce annotation costs and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random data sampling is used for labeling, then data selection is simple and fast, but a relatively large amount of labeling is required to achieve desired performance
Solution Approach 1:
The patent changes the parameter of data selection from random sampling to difficulty-based sampling. By computing difficulty scores for unlabeled data points and selecting those with intermediate difficulty levels, the system achieves more efficient labeling with fewer annotated examples, directly resolving the contradiction between labeling efficiency and data quantity requirements
Solution Approach 2:
The system implements feedback by using the trained model's predictions and loss values to compute difficulty scores, which then guide the selection of data for the next labeling round. This closed-loop approach ensures that labeling efforts are continuously optimized based on model performance, improving productivity while reducing the total amount of labeled data needed
2Reliability
If all unlabeled data is labeled to achieve high model performance, then model accuracy improves, but annotation costs increase significantly
Solution Approach 1:
The patent applies local quality by differentiating data points based on their difficulty characteristics and selectively labeling only those with intermediate difficulty scores. Instead of uniformly labeling all data, the system focuses annotation resources on the most valuable subset, reducing annotation costs while maintaining model performance through targeted data selection
Solution Approach 2:
The system performs partial action by labeling only a portion of the unlabeled data—specifically those with intermediate difficulty—rather than all available data. This selective approach achieves sufficient model performance without the excessive annotation costs associated with labeling the entire dataset
3Adaptability or versatility
If difficult data is presented early in training, then the model learns from diverse examples, but training stability decreases and convergence is slower
Solution Approach 1:
The patent applies preliminary action by computing difficulty scores and sorting data before training begins. The system prepares the training dataset in advance with difficulty annotations, enabling the model to systematically progress from easy to difficult examples without incurring additional computational overhead during training, thus maintaining both diversity and efficiency
4Stability of the object's composition
If easy data is presented first in curriculum learning, then training stability improves, but the model may fail to learn complex patterns effectively
Solution Approach 1:
The patent implements dynamics by making the training curriculum adaptive rather than static. The system dynamically adjusts the difficulty of training examples based on model performance and difficulty scores, allowing the curriculum to evolve during training. This ensures both stability through gradual progression and adaptability through dynamic adjustment of data difficulty
Data Source
AI summary
Computer systems and computer implemented methods for training a machine learning model are provided that includes: selecting seed data from an unlabeled dataset; labeling the seed data and storing the labeled seed data in a data store; training the machine learning model in an initial iteration using the labeled seed data, where the machine learning model is trained to select a next subset of the unlabeled dataset; selecting a next subset of the unlabeled dataset; computing difficulty scores for at least the next subset of the unlabeled dataset; labeling the next subset of the unlabeled data; and training the machine learning model in a second iteration using the labeled next subset of the unlabeled dataset. The machine learning model is generally trained to select the next subset of the unlabeled dataset for a subsequent training iteration by presenting the labeled next subset of the unlabeled dataset in an order sorted based on the difficulty scores.