Attention-Based Classifier Training for Active Log Annotation
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Solution Overview
Problem
The collection of high-quality training data for machine learning solutions in industrial environments is costly and time-consuming, especially in complex systems like log file diagnostics, where manual effort from domain experts is required, and existing tools lack efficient active learning techniques for data annotation.
Innovation Solution
A method and system using an attention mechanism-based classifier with self-attention generative adversarial networks to process log entries, allowing for active learning and iterative annotation, reducing the need for extensive domain expert involvement by automatically selecting and labeling data points, and providing visual feedback for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data annotation by domain experts is used to collect training data, then data quality is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system enables self-service data annotation through the generative AI model that automatically generates synthetic training data with ground truth labels, eliminating the need for manual annotation by domain experts while maintaining high data quality
Solution Approach 2:
The system performs preliminary data preparation by generating synthetic training data in advance using simulated environments and physics engines, so that when real data is needed, high-quality annotated data is already available without requiring time-consuming manual annotation
2Measurement precision
If more training data is collected to improve model accuracy, then classification precision is improved, but data collection cost and complexity increase
Solution Approach 1:
The system creates synthetic copies of training data through generative AI models that simulate real-world scenarios, allowing accumulation of large volumes of diverse training data without the complexity of collecting and annotating equivalent real data
Solution Approach 2:
The generative AI system serves multiple functions simultaneously: it generates synthetic data, creates ground truth labels, simulates various operating conditions, and produces diverse scenarios all in one unified platform, reducing overall system complexity
3Measurement precision
If domain experts are involved in training data generation, then data accuracy is improved, but resource cost increases due to expert availability and expense
Solution Approach 1:
The system replaces expensive domain experts with an automated generative AI model that produces accurate annotated data independently, eliminating the need to hire or consult specialized experts while maintaining high data accuracy through physics-based simulations
Solution Approach 2:
The system substitutes human domain experts with computational models including physics engines and generative AI algorithms, replacing manual expert judgment with automated scientific computing that is both accurate and cost-effective
Data Source
AI summary
Blocks of spatially structured information such as log files or images are processed in a training loop for an attention-based classifier, using an active learning approach. First, the classifier provides a predicted label and an attention map for each classified block. Blocks are selected from the classified blocks if the output of the classifier for the respective block meets a selection criterion. The selected blocks are then displayed to a user together with the predicted label and a visual representation of the attention map. Based on these changes, the classifier is retrained. The method allows for an automatic, intelligent selection of a small number of data points that need to be labeled by a domain expert. The domain expert does not need to collect the training data a priori, but systematically and iteratively gets asked for training examples that are then directly used by the machine learning algorithm for learning.