Spectral Adapter Training for Cross-Domain Transformer Adaptation
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Solution Overview
Problem
Transformer machine learning models struggle to accurately recognize features in domains different from their initial training domain due to the impracticality of conventional adapter-based approaches, which require exhaustive fine-tuning and significant computing resources, and fail to consider the transfer of periodic and aperiodic features.
Innovation Solution
Incorporating a spectral neural network into the adapter model to train transformer models on new domains, allowing for the re-modulation of existing weights and sharing parameters across tasks, thereby enhancing adaptability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional adapter-based approaches are used to train transformer models on new domains, then the model can adapt to different domains, but the training requires exhaustive fine-tuning and significant computing resources
Solution Approach 1:
The adapter model is segmented into distinct spectral processing components (Fourier transform module, spectral filtering module, inverse Fourier transform module) that operate independently on different frequency components of the input data, allowing efficient parallel processing and reducing overall computational burden
Solution Approach 2:
Instead of fully fine-tuning the entire transformer model, the invention applies partial action by training only the spectral adapter components on top of the pre-trained model, achieving domain adaptation with significantly reduced computational resources while maintaining model performance
2Adaptability or versatility
If conventional adapter-based approaches are used for domain adaptation, then the model can recognize features in new domains, but the approach fails to consider the transfer of periodic and aperiodic features
Solution Approach 1:
The invention transforms the adaptation problem from the time/spatial domain to the frequency domain using Fourier transforms, enabling separate processing and transfer of periodic (frequency-based) and aperiodic (residual) features, thereby preserving comprehensive feature information during domain adaptation
Solution Approach 2:
The spectral adapter dynamically adjusts frequency-domain parameters (spectral coefficients) based on the target domain characteristics, allowing the model to capture and transfer both periodic patterns and aperiodic variations effectively across different domains
3Productivity
If the spectral adapter is trained during refinement training phase with unchanged original weights, then the training efficiency is improved, but the model must maintain parallel execution of adapter and original network
Solution Approach 1:
The spectral adapter is designed to work in parallel with the pre-trained transformer model during inference, allowing the original model weights to remain fixed and pre-computed, while only the adapter components require training, thus improving training efficiency without requiring full model retraining
Solution Approach 2:
The spectral adapter acts as an intermediary layer between the input data and the pre-trained transformer model, processing and transforming the input in the frequency domain before passing it to the original model, thereby enabling efficient domain adaptation while maintaining the integrity of the original model architecture
Data Source
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AI summary
A device may train a spectral adapter including a spectral neural network during a refinement training phase using a training dataset of the first domain, subsequent to the transformer training phase, wherein the first domain is different from the second domain, the set of multi-level perceptron weights being unchanged during the refinement training phase. A device may configure the feature identification machine learning model to execute the spectral adapter in parallel with the multi-level perceptron to yield an adapted feature identification machine learning model. A device may identify, using the adapted feature identification machine learning model, the features in the input dataset of the first domain.