Neural Network Training Using Time-Ordered Modality Signals
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Solution Overview
Problem
Current neural network training methods face challenges in improving performance across different domains, particularly in aligning and utilizing modality signals from various sources like audio and vision, which are not inherently synchronized in time, affecting the accuracy of tasks such as video classification.
Innovation Solution
A neural network training method that aligns target modality signals from one domain with auxiliary signals from another domain by using multiple neural network models to extract characteristic information and estimate time order, updating weights based on loss signals to enhance performance across different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple neural network models are used to process different modality signals, then the classification performance is improved, but the device complexity increases
Solution Approach 1:
The system divides the processing of different modality signals into separate neural network models, with each model specialized for a specific modality type. This segmentation allows each model to optimize its processing for its designated modality while maintaining overall system performance.
Solution Approach 2:
The training device is designed to handle multiple types of modality signals simultaneously through multiple neural network models, making it a universal system that can process audio, visual, and other signal types through a unified multi-modal framework.
2Measurement precision
If auxiliary modality signals are integrated to enhance training, then the recognition accuracy is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The system introduces an intermediary mechanism that aligns auxiliary modality signals with target modality signals in time order, facilitating their integration. This intermediary processing step enables the system to handle the complexity of multi-modal alignment while improving recognition accuracy.
Solution Approach 2:
The system performs preliminary alignment of auxiliary modality signals with target modality signals before integration into the neural network training process. This preliminary action of time-order alignment simplifies the subsequent processing and integration of multi-modal data.
3Measurement precision
If time order estimation of auxiliary signals is performed, then the synchronization accuracy is improved, but the loss of time increases
Solution Approach 1:
The system performs time order estimation of auxiliary modality signals as a preliminary step before main processing. By estimating the time order in advance, the system achieves accurate synchronization without requiring extensive time-consuming adjustments during the main training process.
Solution Approach 2:
The system efficiently processes time order estimation by skipping unnecessary intermediate steps and directly determining the temporal relationships between auxiliary and target modality signals, reducing the overall time loss while maintaining synchronization accuracy.
Data Source
AI summary
An apparatus and a method for the disclosure relates to an artificial intelligence (AI) system that simulates functions such as recognition and determination of the human brain by using a machine training algorithm such as deep learning and an application of the AI system are provided. A neural network training method includes obtaining target modality signals of a first domain aligned in a time order and auxiliary modality signals of a second domain that are not aligned in the time order, extracting characteristic information of the target modality signals using a first neural network model, estimating the time order of the auxiliary modality signals using a second neural network model, and training the first neural network model based on a result of the estimation and the characteristic information.