Multi-Discriminator Neural Network Training for Anomaly Detection
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Solution Overview
Problem
Current recognition systems face challenges in achieving high accuracy for data recognition, particularly in anomaly detection tasks, as they often suffer from overtraining and limited generalization ability, which affects their performance in real-world applications such as monitoring systems.
Innovation Solution
The proposed solution involves an information processing apparatus that utilizes a neural network (NN) and multiple discriminators to improve recognition accuracy. This apparatus includes a processing unit for obtaining output data, a determination unit for assessing errors in discrimination results, and training units for both the recognition unit and the discriminators. The training process involves backpropagation of errors to enhance the NN's performance and the discriminators' ability, using techniques like dropout to prevent overtraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If training is performed using a single discriminator, then the training process is simple, but the recognition accuracy and generalization ability are limited
Solution Approach 1:
The patent divides the discrimination task into multiple independent discriminators, each responsible for different aspects of the recognition problem. This segmentation allows the system to achieve higher accuracy by combining multiple specialized discriminators rather than using a single general-purpose one, directly resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent combines multiple discriminators into a unified training framework where their outputs are integrated through error backpropagation. By merging the capabilities of multiple discriminators while maintaining their individual specialization, the system achieves improved recognition accuracy without proportionally increasing overall system complexity.
2Measurement precision
If training data is extensively used to improve recognition accuracy, then the model becomes overtrained, but reducing training data limits accuracy improvement
Solution Approach 1:
The patent implements error backpropagation from multiple discriminators to the neural network, creating a feedback mechanism that continuously adjusts the model based on performance errors. This feedback loop allows the system to learn effectively from training data without overfitting, as the multi-discriminator framework provides diverse error signals that promote better generalization.
Solution Approach 2:
The training process dynamically adjusts the weighting and contribution of each discriminator based on their individual performance. This dynamic adaptation allows the system to optimize its use of training data, preventing overtraining by automatically reducing reliance on discriminators that show signs of overfitting while maintaining accuracy through the collective input of all discriminators.
3Adaptability or versatility
If multiple discriminators are used to improve discrimination ability, then the training complexity increases, but the error determination and backpropagation processes become more complex
Solution Approach 1:
The patent designs a universal training framework that handles multiple discriminators through a common error determination and backpropagation mechanism. This multi-functional training process can accommodate any number of discriminators without requiring fundamentally different approaches, thereby improving discrimination ability while controlling the increase in training complexity through standardized procedures.
Data Source
AI summary
There is provided with an information processing apparatus. A processing unit obtains output data by inputting training data to a recognition unit. A determination unit determines an error in a discrimination result for the training data obtained by inputting the output data to a plurality of discriminators. A first training unit trains the recognition unit based on the error in the discrimination result.