Incremental Anomaly Detection via Teacher-Student Knowledge Distillation
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Solution Overview
Problem
Current neural network approaches are inefficient and inaccurate in detecting and localizing anomalies in various scenes and images, as they require separate training for each type of anomaly and lack the capability to adapt to new scenes effectively.
Innovation Solution
A system comprising a teacher and student neural network model, where the teacher model is trained on non-anomalous images using unsupervised learning and knowledge distillation, enabling the student model to learn new scenes without increasing memory footprint, allowing for anomaly detection and localization in a single training flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate training is performed for each type of anomaly, then detection precision for specific anomaly types is improved, but training time and system complexity increase significantly
Solution Approach 1:
The patent combines multiple anomaly detection tasks into a single unified training process. The neural network is trained to detect multiple types of anomalies simultaneously by processing images with different anomaly types through the same network architecture in one training flow, eliminating the need for separate training sessions for each anomaly type.
Solution Approach 2:
The neural network is designed with universal capabilities to handle multiple anomaly detection tasks. The network learns to identify different anomaly types through a single training process, making it a multi-functional system that can detect various anomalies without requiring specialized training for each type.
2Adaptability or versatility
If the neural network is trained to identify all types of anomalies in all scenes, then detection coverage is improved, but training efficiency and resource requirements worsen
Solution Approach 1:
The patent merges the training of multiple scene types and anomaly types into a single training flow. The neural network processes images from different scenes containing different anomaly types simultaneously during training, achieving broad detection coverage without requiring separate training processes for each scene-anomaly combination.
3Adaptability or versatility
If incremental learning is applied to learn new scenes, then adaptability to new scenes is improved, but memory footprint increases
Solution Approach 1:
The patent uses knowledge distillation where a teacher network (pre-trained on previous scenes) generates pseudo-labels for student networks (training on new scenes). This copying of knowledge from teacher to student allows the student network to learn new scenes without requiring extensive additional memory, as it leverages the compressed knowledge representation from the teacher network.
4Measurement precision
If extensive retraining is performed for new scenes, then detection accuracy for new scenes is improved, but time and computational resources required increase
Solution Approach 1:
The patent performs preliminary training on a diverse set of scenes and anomaly types in advance. This pre-training equips the neural network with generalizable knowledge that can be quickly adapted to new scenes through incremental learning, reducing the need for extensive retraining when encountering new scene types.
Solution Approach 2:
The patent employs incremental learning that allows the neural network to adapt to new scenes by adjusting its parameters efficiently. The network modifies its weights and biases incrementally when exposed to new scene data, achieving high detection accuracy for new scenes without requiring complete retraining from scratch.
Data Source
AI summary
Anomalies in multiple different scenes or images can be detected and localized in a single training flow of a neural network. In various examples, incremental learning can be applied to a given system or network, such that the system or network can learn the distribution of new scenes in a single training flow. Thus, in some cases, when an anomalous image from a new scene is given as input to the network, the network can detect and localize the anomaly.