End-to-End Modeling With Code Enhancement Layer
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Solution Overview
Problem
Conventional end-to-end modeling methods, such as the Encode-Decode model, result in a significant difference between the encoded feature sequence and the target labeling unit, leading to reduced accuracy in modeling due to incomplete information processing.
Innovation Solution
A target-based end-to-end model is introduced, incorporating a code enhancement layer to add target unit information to the feature sequence and a filtering layer to eliminate redundant information, thereby improving the accuracy of modeling by enhancing the encoded feature sequence with more complete information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the conventional Encode-Decode model is used for end-to-end modeling, then the model structure is simple, but the encoded feature sequence has a relatively big difference with the target labeling unit, resulting in reduced modeling accuracy
Solution Approach 1:
The encoding process is segmented into multiple encoding layers (first encoding layer, second encoding layer, etc.), where each layer progressively refines the feature sequence. This segmentation allows the model to capture information at different levels of abstraction, improving the match between encoded features and target labeling units while maintaining manageable complexity through modular layer design.
Solution Approach 2:
The patent implements nested encoding structures where inner encoding layers are contained within outer encoding layers. The first encoding layer processes features, then the second encoding layer further processes the output of the first layer, creating a nested hierarchy. This nesting allows progressive refinement of feature representations, improving modeling accuracy by capturing both fine-grained and coarse-grained patterns.
2Loss of information
If multiple encoding layers are added to improve the accuracy of encoded feature sequence, then the information completeness is improved, but the computational complexity increases
Solution Approach 1:
The first encoding layer performs preliminary encoding of the feature sequence before the second encoding layer processes it. This preliminary action extracts and preserves important information in an intermediate representation, reducing information loss. By performing encoding in staged preliminary actions rather than a single complex operation, the model maintains information completeness while managing computational complexity through progressive processing.
Data Source
AI summary
A method and a system for end-to-end modeling are provided. The method includes: determining a topological structure of a target-based end-to-end model, where the topological structure includes an input layer, an encoding layer, an code enhancement layer, a filtering layer, a decoding layer and an output layer; the code enhancement layer adds information of a target unit to a feature sequence outputted by the encoding layer, the filtering layer filters a feature sequence added with the information of the target unit; collecting multiple pieces of training data; and training parameters of the target-based end-to-end model by using the multiple pieces of the training data.