Modular Machine Learning Framework for Streaming Data Adaptation
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Solution Overview
Problem
Traditional machine learning approaches require large amounts of labeled data for training, which is impractical in domains like naval, space, and underwater environments, and are not adaptable to changing data distributions, leading to poor performance in real-world applications.
Innovation Solution
A machine learning framework that combines a hybrid replay module, task-specific network, and architecture optimization module to train models in real-time using live streaming data, addressing class imbalance and adapting to evolving sensor data for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning approaches are used with large amounts of labeled data for training, then model accuracy is improved, but data collection cost and time increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training on available limited labeled data and pre-processing streaming data through buffering and augmentation before model training, reducing the need for extensive real-time labeled data collection
Solution Approach 2:
The system creates synthetic copies of limited labeled data through data augmentation techniques including noise injection, signal manipulation, and generative models to artificially expand the training dataset without additional field data collection
2Stability of the object's composition
If models are trained offline on static datasets, then training stability is improved, but adaptability to changing real-world data distributions deteriorates
Solution Approach 1:
The system transitions from static offline training to dynamic online learning by continuously adapting model parameters to evolving data distributions through streaming data processing and incremental learning mechanisms
Solution Approach 2:
The system implements feedback loops where model predictions are continuously evaluated against new streaming data, and performance metrics feed back into the training process to adjust parameters and improve adaptability to changing distributions
3Adaptability or versatility
If models are trained in real-time on streaming data, then adaptability to changing data is improved, but computational resource requirements increase
Solution Approach 1:
The system applies partial action by selectively processing only the most informative portions of streaming data through buffering strategies and sampling, rather than processing every data point, reducing computational overhead while maintaining adaptability
Solution Approach 2:
The system segments the continuous streaming data into manageable batches or chunks for incremental processing, allowing efficient utilization of computational resources through staged training updates rather than continuous full-data processing
4Measurement precision
If complex architectures are used to handle evolving sensor data, then model performance is improved, but system complexity increases
Solution Approach 1:
The system achieves improved performance through parameter optimization and tuning of existing architectural components rather than fundamentally complex architectures, using techniques like learning rate scheduling, regularization parameters, and feature selection
Data Source
AI summary
In an example, a system includes processing circuitry in communication with storage media. The processing circuitry is configured to execute a machine learning system including at least a first module, a second module and a third module. The machine learning system is configured to train one or more machine learning models. The first module is configured to generate augmented input data based on the streaming input data. The second module includes a machine learning model configured to perform a specific task based at least in part on the augmented input data. The third module configured to adapt a network architecture of the one or more machine learning models based on changes in the streaming input data.