Batch Active Learning for Deep Model Training
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Solution Overview
Problem
Current machine learning model training systems using active learning are inefficient due to excessive re-training and querying, especially with deep learning models, and fail to account for noisy or incorrect labels, leading to high computational costs and resource usage.
Innovation Solution
Implementing a batch-based active learning approach where the learning algorithm queries and retrains using batches of labeled examples instead of single examples, and incorporating a denoising layer to handle noisy data, reducing processing power and query frequency while improving model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the learning algorithm queries and retrains using single labeled training examples, then the model can be trained with minimal labeled data, but the computational time and resource usage increase significantly
Solution Approach 1:
The patent segments the training process into batches of training examples rather than processing single examples individually. The learning algorithm selects and processes multiple unlabeled training examples in batches, queries their labels together, and retrains the model on each batch. This segmentation reduces the total number of separate query and retrain cycles while maintaining effective model training.
2Productivity
If the system queries the information source frequently for individual labels, then the model can adapt quickly to new data, but the number of queries and associated costs increase
Solution Approach 1:
The patent merges multiple individual label queries into a single batch query operation. Instead of querying the information source separately for each training example, the system collects multiple unlabeled examples, queries their labels in one batch operation, and then processes them together. This merging significantly reduces the total number of queries to the information source while maintaining model adaptability.
3Reliability
If the system retrains the model after each single label acquisition, then the model remains up-to-date with latest data, but the re-training computational cost accumulates
Solution Approach 1:
The patent segments the retraining process by performing model retraining on batches of labeled examples rather than after each individual label acquisition. This batching approach consolidates multiple retraining operations into fewer, more efficient batch retraining cycles, reducing the cumulative computational overhead while maintaining model accuracy through regular updates.
4Productivity
If the system processes training examples in batches, then computational efficiency improves, but the complexity of batch selection and processing increases
Solution Approach 1:
The patent incorporates feedback mechanisms to manage batch processing complexity. The learning algorithm uses the model's performance and uncertainty estimates to feedback on which unlabeled training examples should be selected for the next batch. This feedback-driven selection process automates the batch composition, managing the complexity of batch processing while maintaining training efficiency through intelligent example selection.
Data Source
AI summary
A system for training a machine learning model using a batch based active learning approach. The system includes an information source and an electronic processor. The electronic processor is configured to receive a machine learning model to train, an unlabeled training data set, a labeled training data set, and an identifier of the information source. The electronic processor is also configured to select a batch of training examples from the unlabeled training data set and send, to the information source, a request for, for each training example included in the batch, a label for the training example. The electronic processor is further configured to, for each training example included in the batch, receive a label, associate the training example with the label, and add the training example to the labeled training data set. The electronic processor is also configured to train the machine learning model using the labeled training data.