Neural Network Domain Adaptation via Adapter Layers
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Solution Overview
Problem
Face recognition systems trained in one geographical region often experience performance drops when deployed in another region due to differences in facial characteristics and features.
Innovation Solution
The implementation of a domain adaptation technique that modifies a pre-trained model by adding additional layers with trainable parameters in a cascaded or parallel configuration, allowing the model to adjust weights based on features from the target domain while keeping original weights unchanged.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-trained model is trained on large-scale data from a particular country or geographical region, then the model performance is improved in the home country, but the performance drops when deployed in another country or geographical region
Solution Approach 1:
The model architecture is segmented into two distinct parts: a pre-trained backbone network that processes input images and extracts features, and a domain-specific adapter layer that adapts these features to the target domain. This segmentation allows the model to maintain general feature extraction capabilities while adding domain-specific adaptation, thereby resolving the contradiction between home-country optimization and cross-domain performance.
Solution Approach 2:
An adapter layer is introduced as an intermediary component between the pre-trained model and the output layer. This adapter acts as a mediator that transforms features from the source domain representation to the target domain representation, enabling the model to maintain high accuracy in the home country while adapting to perform well in other geographical regions.
2Adaptability or versatility
If additional layers with trainable parameters are added to adapt the pre-trained model to the target domain, then the cross-domain performance is improved, but the device complexity increases
Solution Approach 1:
Instead of retraining the entire pre-trained model for the target domain (excessive action), only a partial component - the adapter layer - is trained and adapted. This partial action approach achieves domain adaptation with minimal additional complexity, avoiding the need to modify or retrain the entire model architecture while still improving target domain performance.
Solution Approach 2:
The adapter layer is nested within the existing pre-trained model architecture, specifically positioned between the feature extraction layers and the output layer. This nesting allows the adapter to be integrated seamlessly into the existing model structure, adding adaptation capability without significantly increasing overall model complexity or requiring complete architectural redesign.
Data Source
AI summary
A computer-implemented method comprising: training a pre-trained neural network that comprises: an input layer; a plurality of hidden layers, wherein each of the plurality of hidden layers has one or more nodes, wherein each of said one or more nodes has an associated weight trained based on data from a source domain; and an output layer. Training the pre-trained neural network comprises: introducing at least one additional layer to the plurality of hidden layers, wherein said additional layer has one or more nodes having associated weights; keeping weights of the nodes in the plurality of hidden layers of the pre-trained neural network unchanged; inputting data from a target domain to the input layer; and adjusting weights of the one or more nodes in the at least one additional layer based on features obtained at the output layer.