Transformer Classification With Spectral Global-Local Feature Processing
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Solution Overview
Problem
Transformer models face challenges in efficiently capturing both global and local information from large datasets, leading to increased computational complexity and loss of informational integrity.
Innovation Solution
The integration of spectral transformations, specifically using Hartley Transform with convolutional operators in initial layers, followed by deeper attention layers, to preprocess datasets, allowing for the capture of both global and local features with reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transformer models use multiple attention layers to process large datasets, then the ability to capture contextual information is improved, but computational complexity increases
Solution Approach 1:
The patent segments the embedding space into frequency components using spectral transformations (Hartley Transform). The periodic information processor handles global features while the aperiodic information processor handles local features, dividing the computational task into manageable segments that reduce overall complexity while preserving information integrity.
Solution Approach 2:
The patent introduces spectral transformations as an intermediary mechanism between the input data and the attention layers. This intermediary transforms the data into frequency domain representations, allowing the model to capture both global and local information more efficiently without requiring excessive attention layers.
2Measurement precision
If transformer models process large datasets with attention mechanisms, then classification accuracy can be improved, but processing speed decreases
Solution Approach 1:
By segmenting the processing into periodic (global) and aperiodic (local) components, the model can process different types of information in parallel through separate processors, improving processing speed while maintaining classification accuracy through comprehensive feature capture.
Solution Approach 2:
The patent changes the representation parameters of the data by applying spectral transformations. This parameter transformation allows the model to work with frequency domain representations that can be processed more efficiently, balancing accuracy and speed.
3Device complexity
If spectral transformations are applied to preprocess datasets, then computational complexity is reduced, but the ability to capture local features may be compromised
Solution Approach 1:
The patent segments the spectral information into periodic and aperiodic components, with dedicated processors for each type. The aperiodic information processor specifically handles local features, ensuring they are not lost while still benefiting from the computational efficiency of spectral transformations.
Solution Approach 2:
The spectral module serves multiple functions: it reduces computational complexity through efficient frequency domain processing while simultaneously preserving both global and local feature information through its dual-processor architecture, making it a universal solution for various feature extraction needs.
Data Source
AI summary
A method may embed the input dataset into a first embedding space. A method may input the first embedding space into a spectral module including a periodic information processor and an aperiodic information processor. A method may identify global features in the input dataset using the periodic information processor based on a first subset of the first embedding space. A method may identify first local features in the input dataset using the aperiodic information processor based on a second subset of the first embedding space, wherein the first subset and the second subset are different. A method may combine the global features and the first local features into a dataset of classified features of the input dataset.