Student-Teacher Neural Network for Anomaly Detection with Limited Labeled Data
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Solution Overview
Problem
Existing anomaly detection systems in digital environments rely on unsupervised learning methods, which are resource-intensive and often require large volumes of data to achieve accurate results, limiting their effectiveness with limited data and high computational resource demands.
Innovation Solution
The implementation of a semi-supervised machine learning approach that combines supervised and unsupervised methods, using a student-teacher neural network framework to optimize anomaly detection. This approach utilizes both labeled and unlabeled data to improve model accuracy and efficiency, allowing for higher anomaly detection rates with less data and reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If unsupervised learning methods are used for anomaly detection, then the system can operate with limited labeled data, but the computational resource consumption increases significantly
Solution Approach 1:
The patent segments the training process into two distinct phases: unsupervised pre-training on large volumes of unlabeled data to learn general patterns, followed by supervised fine-tuning on a small subset of labeled data to specialize in anomaly detection. This segmentation allows the model to efficiently utilize limited labeled data while avoiding excessive computational resource consumption during the training phase.
Solution Approach 2:
The patent applies preliminary unsupervised learning action by pre-training the model on unlabeled data before the actual supervised anomaly detection task. This preliminary action enables the model to develop robust feature representations in advance, reducing the computational burden during the subsequent supervised training phase with limited labeled data.
2Quantity of substance
If unsupervised learning methods are used for anomaly detection, then the system can process large volumes of data, but the detection accuracy is insufficient
Solution Approach 1:
The patent merges unsupervised learning and supervised learning approaches into a unified two-stage training framework. The model first undergoes unsupervised pre-training on large volumes of unlabeled data to capture general patterns, then undergoes supervised fine-tuning on a small labeled dataset to achieve high anomaly detection accuracy. This combination allows the system to process large data volumes while maintaining high detection precision.
Solution Approach 2:
The patent ensures continuity of useful action by maintaining the learned representations from unsupervised pre-training throughout the supervised fine-tuning phase. The model continuously refines its anomaly detection capabilities by building upon the foundational patterns learned from large volumes of unlabeled data, thereby achieving high accuracy without discarding the benefits of large-scale data processing.
3Adaptability or versatility
If traditional unsupervised methods are used, then the system can work with any data, but the model complexity and training time increase
Solution Approach 1:
The patent segments the complex training process into manageable phases: unsupervised pre-training on diverse unlabeled data to ensure adaptability, followed by supervised fine-tuning to reduce complexity. This segmentation allows the model to work with various data types while keeping the overall training complexity可控 through structured progression.
Solution Approach 2:
The patent performs preliminary unsupervised learning action on diverse unlabeled data before the supervised phase, enabling the model to adapt to different data distributions in advance. This preliminary action reduces the complexity of the subsequent supervised training by pre-establishing robust feature representations that generalize across different data types.
Data Source
AI summary
An example method includes for each training iteration in a series of training iterations: receiving, for a first set of unlabeled metrics data, a set of respective labels associated with an account in a set of accounts. The first set of unlabeled metrics data and a second set of unlabeled metrics data can be processed to generate network inputs representing a respective account in the set of accounts. The network inputs can be processed using a teacher neural network that generates an anomaly prediction output for each network input. A student neural network can be trained to optimize a loss function, which can include minimizing a loss term measuring a difference between the anomaly prediction output and the student network's proposed anomaly prediction output and minimizing a loss term measuring a difference between the proposed anomaly prediction output and the account's associated label.