Neural Network Parameter Averaging for Domain Adaptation
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Solution Overview
Problem
Traditional unsupervised transfer learning methods for neural networks face challenges in accurately recognizing images across different domains due to errors in pseudo hard labels, leading to poor feature extraction and recognition accuracy, especially when dealing with varying ambient conditions and camera parameters.
Innovation Solution
The method involves training a target neural network by taking time average values of parameters from a first neural network and an average network, where the first neural network is supervised by the output of the average network, and vice versa, to reduce correlation defects and improve feature extraction accuracy across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional unsupervised transfer learning methods are used to train neural networks on source domain and apply to target domain, then the neural network can be trained without labeled target domain data, but the recognition accuracy deteriorates due to errors in pseudo hard labels
Solution Approach 1:
The patent introduces an average network as an intermediary component that generates soft labels for supervising the first neural network. This average network is trained on both source and target domains, providing more accurate supervision signals than traditional pseudo hard labels. The soft labels from the average network serve as a mediator between the labeled source domain and unlabeled target domain, reducing the accuracy deterioration caused by label errors.
Solution Approach 2:
The patent changes the parameter representation from hard labels (discrete class identifiers) to soft labels (probability distributions). This parameter transformation allows for more nuanced supervision signals that capture uncertainty and partial class memberships, thereby improving recognition accuracy while maintaining the unsupervised transfer learning framework.
2Ease of manufacture
If neural networks are trained with pseudo hard labels from source domain, then training can proceed without target domain labels, but feature extraction accuracy deteriorates due to label errors
Solution Approach 1:
The average network acts as an intermediary that generates more accurate soft labels for feature extraction supervision. These soft labels from the average network provide better guidance for the first neural network's feature extraction process, reducing the accuracy loss that would otherwise occur when using erroneous pseudo hard labels.
Solution Approach 2:
The patent creates a composite training approach that combines supervised learning from source domain labels with semi-supervised learning from average network soft labels. This composite methodology integrates multiple supervision sources, improving feature extraction accuracy while maintaining training feasibility without requiring abundant target domain labels.
3Measurement precision
If traditional supervised training is applied to target domain with limited labeled data, then recognition performance can be improved, but the labeling cost and time increase significantly
Solution Approach 1:
The system enables self-service training where the average network automatically generates soft labels for the first neural network without requiring manual annotation of target domain data. This self-supervised mechanism allows the model to improve recognition performance through automated label generation, eliminating the need for time-consuming manual labeling while still achieving accurate target domain recognition.
Solution Approach 2:
The patent implements a feedback loop where the average network continuously refines its soft label predictions based on performance on target domain data, which in turn improves the training of the first neural network. This feedback mechanism allows the system to progressively improve recognition performance using only unlabeled target domain data, avoiding the need for iterative manual labeling.
4Adaptability or versatility
If neural networks are trained to adapt to varying ambient conditions and camera parameters, then adaptability to different domains improves, but the complexity of handling domain variations increases
Solution Approach 1:
The average network serves as a universal component that handles multiple domain adaptation scenarios simultaneously. It processes both source and target domain data through a unified framework, generating soft labels that account for various ambient conditions and camera parameters. This multi-functional approach improves domain adaptability without requiring separate complex mechanisms for each type of domain variation.
Solution Approach 2:
The patent uses parameter changes in the form of soft label probability distributions to adapt to varying ambient conditions and camera parameters. By adjusting the soft label parameters based on domain characteristics rather than restructuring the entire system, the method achieves high domain adaptability while keeping the handling complexity relatively low.
Data Source
AI summary
The present disclosure discloses an image processing method and related device thereof. The method includes: acquiring an image to be processed; and performing a feature extraction process on the image to be processed using a target neural network so as to obtain target feature data of the image to be processed, wherein parameters of the target neural network are time average values of parameters of a first neural network which is obtained from training under supervision by a training image set and an average network, and parameters of the average network are time average values of parameters of a second neural network which is obtained from training under supervision by the training image set and the target neural network. A corresponding device is also disclosed. Feature data of image to be processed are obtained via the feature extraction process performed on the image to be processed.