Neural Network Pattern Recognition via Domain Vector Segmentation
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Solution Overview
Problem
Existing pattern recognition systems based on Neural Networks (NNs) are vulnerable to domain variability, leading to poor performance when training and evaluation data mismatch, and current methods struggle to compensate for domain variability without parallel recordings across different domains.
Innovation Solution
A pattern recognition apparatus and method that trains an NN model using feature vectors and domain vectors to generate NN parameters, allowing verification of whether a pair of feature vectors belongs to the same individual, even in a new domain, by predicting target domain vectors using existing unlabeled data from various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a NN-based pattern recognition system is trained using data from a specific domain, then it achieves good performance on in-domain data, but it deteriorates performance when evaluated on out-of-domain data due to domain variability
Solution Approach 1:
The patent segments the training data into multiple domains and introduces domain vectors to represent each domain. The NN is trained to recognize both individual features and domain characteristics separately, allowing it to adapt to different domains by combining individual features with domain information rather than treating all data uniformly.
Solution Approach 2:
The patent changes the parameter representation by introducing domain vectors as additional input features. Instead of training the NN to handle domain variability implicitly through raw data variations, the system explicitly represents domain characteristics as separate parameters (domain vectors) that are concatenated with individual feature vectors, enabling the model to adapt to different domains.
2Reliability
If a large amount of in-domain labeled data is collected to train the NN, then the pattern recognition performance improves, but the cost and complexity of data collection and labeling increase significantly
Solution Approach 1:
The patent introduces domain vectors as an intermediary representation that captures domain-specific characteristics. Instead of requiring大量 labeled in-domain data, the system uses domain vectors (which can be derived from unlabeled data or domain metadata) to bridge the gap between different domains, reducing the need for extensive labeled data collection while maintaining performance.
Solution Approach 2:
The patent creates domain vectors that serve as simplified copies or representations of entire domains. Instead of using the full complexity of labeled in-domain data, the system uses these compact domain vector representations to capture essential domain characteristics, reducing data collection and labeling requirements while preserving the ability to adapt to different domains.
3Ease of operation
If traditional NN training methods are used without domain information, then the training process is simple, but the system cannot compensate for domain variability and achieves poor performance on out-of-domain data
Solution Approach 1:
The patent performs preliminary extraction of domain vectors from training data before the main NN training process. By pre-computing domain representations and storing them as domain vectors, the system prepares domain information in advance, making the main training process relatively simple while ensuring the NN has access to domain characteristics for better out-of-domain performance.
Data Source
AI summary
Provided is a pattern recognition apparatus to provide classification robustness to any kind of domain variability. The pattern recognition apparatus 500 based on Neural Network (NN) includes: NN training unit 501 that trains an NN model to generate NN parameters, based on at least one first feature vector and at least one domain vector indicating one of subsets in a specific domain, wherein, the first feature vector is extracted from each of the subsets, the domain vector indicates an identifier corresponding to the each of the subsets; and NN verification unit 502 that verifies a pair of second feature vectors in the specific domain to output whether the pair indicates same individual or not, based on a target domain vector and the NN parameters.