Domain Adapter for Neural Network Object Recognition
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Solution Overview
Problem
Deep neural networks (DNNs) face performance degradation when applied outside their training domain, requiring costly retraining with labeled data sets, which is difficult to collect, especially in open-set environments where object classes do not perfectly match between source and target domains.
Innovation Solution
A domain adaptation approach using a shared encoder and decoder with a generative adversarial network (GAN) to extract features and classify objects across domains, employing a shared parameter for feature extraction and classification, and a discriminator to estimate image authenticity, allowing for training without complete label information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a deep neural network is trained in a source domain and applied to a target domain, then the model can perform object recognition, but the performance degrades due to domain difference
Solution Approach 1:
The patent introduces a domain adapter module as an intermediary component between the pre-trained DNN and the target domain data. This adapter learns to transform source domain features into target domain representations, enabling the model to adapt to domain differences without requiring complete retraining. The adapter processes the output from the pre-trained network and transforms it to be suitable for the target domain, thus resolving the contradiction between maintaining pre-trained performance and adapting to new domains.
Solution Approach 2:
The patent employs parameter adaptation techniques where the domain adapter modifies network parameters dynamically based on target domain characteristics. By learning domain-specific parameters from target domain data, the model adjusts its feature representations to match the target domain distribution, thereby maintaining high recognition accuracy while achieving domain adaptability.
2Reliability
If labeled data is collected for retraining in a new domain, then recognition performance can be maintained, but the cost and complexity increase significantly
Solution Approach 1:
The patent performs preliminary action by pre-training the main DNN in the source domain before deployment to the target domain. This pre-trained model serves as a foundation that reduces the need for extensive labeled data collection in the target domain. The domain adapter then fine-tunes this pre-trained model using a smaller amount of target domain data, significantly reducing the data collection burden while maintaining performance.
Solution Approach 2:
The patent creates a simplified copy of the full retraining process through the domain adapter. Instead of copying the entire DNN and retraining it from scratch (which would require comprehensive labeled data), the adapter copies only the necessary adaptation functionality, leveraging the pre-trained model's knowledge while adapting to the target domain with minimal labeled data.
3Adaptability or versatility
If the object classes in source and target domains are different, then the model must adapt to new classes, but this requires complete retraining with labeled data
Solution Approach 1:
The patent segments the object recognition system into two parts: a pre-trained DNN that handles general object representation learning from the source domain, and a domain adapter that handles domain-specific adaptations including new object classes in the target domain. This segmentation allows the model to leverage pre-trained knowledge for common objects while quickly adapting to domain-specific classes without requiring complete retraining, thus reducing time loss.
Solution Approach 2:
The domain adapter introduces dynamic adaptability to the model, allowing it to learn and adjust to new object classes in the target domain as they appear. The adapter can dynamically update its parameters based on incoming target domain data, enabling the model to handle class differences flexibly and efficiently without the need for static complete retraining.
Data Source
AI summary
A recognizer including a shared encoder to extract a feature of an input image in a source domain and a target domain; and a shared decoder to classify a class of an object included in the input image based on the feature of the input image, in the source domain and the target domain. A set of object classes of the source domain and a set of object classes of the target domain differ from each other.