Weighted Multi-Model Content Understanding for Small-Sample Fields
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Solution Overview
Problem
In fields with small sample sizes, it is challenging to achieve a better data processing effect using machine learning models due to the limited number of training samples available.
Innovation Solution
Utilize a content understanding model comprising multiple sub-models trained on large-sample data from other fields, and determine a final result by combining the sub-model results with weights based on training data from the target small-sample field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained using only the limited training samples available in a small-sample field, then the training process is simple and fast, but the data processing effect and model accuracy are poor
Solution Approach 1:
The patent applies preliminary action by pre-training a content understanding model using large-scale sample data from multiple fields before applying it to the target small-sample field. This pre-training process prepares the model with general knowledge and capabilities that can be transferred to the specific application scenario, thereby improving data processing effect without requiring extensive training samples in the target field.
Solution Approach 2:
The patent implements universality by designing a content understanding model that can process data across multiple fields simultaneously. The model is trained on diverse sample data from different fields, enabling it to perform various data processing tasks (classification, regression, etc.) in different domains. This multi-functional capability allows the model to effectively handle small-sample scenarios in any target field by leveraging knowledge gained from other fields.
2Measurement precision
If multiple sub-models are used to process data in a small-sample field, then the data processing accuracy is improved, but the model complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the content understanding model into multiple independent sub-models, where each sub-model is responsible for processing data from a specific field or aspect. This segmentation allows the system to handle complex data processing tasks through specialized components, improving accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent implements merging by combining the outputs of multiple sub-models through a weighting mechanism to produce the final processing result. Each sub-model's output is weighted according to its reliability and relevance, and these weighted outputs are merged to form the comprehensive final result. This merging strategy leverages the strengths of multiple specialized models while presenting a unified interface to users.
3Measurement precision
If a content understanding model trained on large-sample data from other fields is applied to a small-sample field, then the data processing effect is enhanced, but the adaptability to the specific target field may be reduced
Solution Approach 1:
The patent applies local quality by assigning different weights to different sub-models based on their performance and relevance to the target field. Each sub-model processes data with appropriate weighting, allowing the system to adapt to local characteristics of the target field while leveraging the general capabilities learned from other fields. This localized adjustment optimizes the balance between general knowledge transfer and field-specific adaptation.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the weights of different sub-models based on the characteristics of the target field and the specific data being processed. These weight parameters are optimized to maximize data processing effect in the target small-sample field, allowing the model to adapt its behavior to different应用场景 while maintaining the benefits of pre-training on diverse data.
Data Source
AI summary
The present application discloses an information processing method, including: acquiring data to be processed in a target field corresponding to a small sample; processing the data to be processed using an initial content understanding model to obtain a processing result, wherein the initial content understanding model includes a plurality of sub-models, each of the plurality of sub-models is obtained based on sample data in a plurality of other fields, each of the plurality of other fields is a field corresponding to a large sample, and the processing result includes a result of processing the data to be processed by each of the plurality of sub-models; and then, determining a final result of processing the data to be processed based on the result of processing the data to be processed by each sub-model and a weight of each sub-model.