Spectral Adapter Training for Cross-Domain Transformer Recognition
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Solution Overview
Problem
Transformer machine learning models struggle to accurately recognize features in domains different from their initial training domain, requiring full retraining which is impractical due to high computational demands and lack of consideration for periodic and aperiodic features, and conventional adapter-based approaches fail to generalize effectively.
Innovation Solution
Incorporating a spectral neural network within a spectral adapter to train transformer models on new domains, allowing for the re-modulation of existing weights and sharing parameters across tasks, thus enhancing adaptability and performance by considering distinct feature types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full retraining of the transformer model is performed to improve feature identification accuracy in a new domain, then the accuracy is improved, but the computational cost and training time increase significantly
Solution Approach 1:
The model is segmented into two parts: the pre-trained transformer model (frozen) and the spectral adapter (trainable). This segmentation allows only the adapter to be trained for domain adaptation, significantly reducing computational cost while maintaining the ability to improve feature identification accuracy in new domains.
Solution Approach 2:
The spectral adapter is extracted as a separate, independent module from the main transformer model. This extracted adapter can be trained independently on new domain data without requiring retraining of the entire model, thereby reducing computational resources while achieving domain adaptation.
2Use of energy by stationary object
If conventional adapter-based approaches are used to adapt to new domains, then computational resources are saved, but the model fails to generalize effectively across different domains
Solution Approach 1:
The spectral adapter transforms the feature representation by learning domain-specific parameter adjustments. It modifies the spectral characteristics of the features to adapt to different domains, enabling effective generalization while keeping the main model frozen and computationally efficient.
Solution Approach 2:
The system combines the pre-trained transformer model with the spectral adapter to create a composite architecture. This composite structure leverages the general feature extraction capabilities of the transformer and the domain-specific adaptation capabilities of the spectral adapter, achieving both efficiency and versatility.
3Loss of time
If the transformer model is trained on a single domain, then training time is reduced, but the model cannot accurately recognize features in different domains
Solution Approach 1:
The transformer model is pre-trained on a source domain beforehand. This preliminary training establishes a solid foundation for feature extraction, and later the spectral adapter can be quickly trained to adapt to target domains, avoiding the need for time-consuming full retraining while enabling cross-domain recognition.
Solution Approach 2:
The spectral adapter acts as an intermediary between the pre-trained transformer model and the target domain data. It mediates the adaptation process by learning to transform features from the source domain representation to the target domain representation, enabling cross-domain recognition without direct retraining of the main model.
Data Source
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.