Domain Adaptation Using Synchronized Encoder-Discriminator Weights
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Solution Overview
Problem
Existing domain adaptation methods face challenges in adapting models from labeled source data to unlabeled or partially labeled target data due to domain shift, where the distribution of training and deployment data differs, leading to reduced model performance in real-world applications.
Innovation Solution
The approach initializes a target encoder and discriminator based on a source encoder, using a first algorithm trained on labeled data, and synchronizes their weights with the source discriminator to adapt to the target domain, allowing for domain adaptation without requiring shared data and reducing data transfer costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain adaptation is performed using traditional methods requiring shared data between source and target domains, then model adaptation effectiveness is improved, but communication costs and data transfer requirements increase
Solution Approach 1:
The system segments the domain adaptation process into independent source domain training and target domain adaptation phases. The source model is trained separately on source domain data, then transferred to the target domain where it adapts using only target domain data, eliminating the need for continuous shared data communication between domains.
Solution Approach 2:
The patent introduces an intermediary adaptation layer that sits between the pre-trained source model and the target domain data. This intermediary component learns to translate or adapt source domain features to target domain features without requiring direct access to source domain data during target domain training, thus reducing communication requirements.
2Loss of energy
If domain adaptation is performed without shared data between source and target domains, then communication costs are reduced, but model adaptation effectiveness deteriorates
Solution Approach 1:
The system performs preliminary action by pre-training the model on the source domain before deployment to the target domain. This pre-training phase establishes a strong foundational model that can then be efficiently adapted to the target domain using only target domain data, compensating for the lack of shared data through advance preparation.
Solution Approach 2:
The patent employs parameter changes by adjusting model parameters during the adaptation phase to better fit the target domain distribution. This includes fine-tuning weights, biases, and other model parameters using target domain data, allowing the model to adapt its parameters to the new domain without requiring shared data.
3Adaptability or versatility
If the target encoder is trained from scratch on target domain data, then adaptation to target domain distribution is improved, but training time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-training the encoder on source domain data before deploying it to the target domain. This pre-trained encoder already possesses general feature extraction capabilities that can be quickly adapted to the target domain, significantly reducing the training time required compared to training from scratch.
Solution Approach 2:
The patent uses copying by initializing the target domain encoder with weights copied from the pre-trained source domain encoder. This copying approach provides a strong starting point for adaptation, allowing the model to leverage previously learned features and reduce the amount of training needed to achieve effective target domain performance.
4Reliability
If the encoder is retrained frequently to adapt to changing target domain data, then model accuracy is improved, but computational resources and training time increase
Solution Approach 1:
The system implements periodic action by performing encoder retraining at scheduled intervals or triggered by specific conditions (such as performance degradation thresholds) rather than continuously. This periodic retraining maintains model accuracy while significantly reducing computational resource consumption compared to frequent or continuous retraining.
Solution Approach 2:
The patent uses parameter changes by selectively updating only certain model parameters during retraining rather than retraining the entire model. This selective parameter update approach maintains accuracy improvements while reducing the computational burden of full model retraining.
Data Source
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AI summary
An apparatus, method and computer program is described comprising: initialising weights of a target encoder based on a source encoder; initialising weights of a target discriminator associated with the target encoder such that the target discriminator is initialised to match a source discriminator associated with the source encoder; applying some of a target data set to the target encoder to generate target encoder outputs; applying the target encoder outputs to the target discriminator to generate a first local loss function output; training the target encoder to seek to increase the first local loss function output; training the target discriminator to seek to decrease the first local loss function output; and synchronising weights of the target discriminator and the source discriminator.