Neural Network Training Using Pseudo-Label Matching
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Solution Overview
Problem
Existing supervised machine learning methods require a large amount of manually generated labeled data, which is time-consuming and prone to human error, especially in vertical applications like industrial vision and medicine, making it difficult to collect recognized target images.
Innovation Solution
An electronic device and method that uses a small amount of artificially labeled data to train a neural network model by generating pseudo-labeled data through a semi-supervised learning architecture, where a teacher model generates initial pseudo-labeled data and a student model refines it through adaptive matching, allowing for the reduction of manually labeled data without compromising performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large amount of manually labeled data is collected to increase machine learning model accuracy, then model performance is improved, but time consumption and human resource consumption increase significantly
Solution Approach 1:
The system enables self-service by allowing the neural network model to generate its own training data through pseudo-labeling. The model processes unlabeled data and generates pseudo-labels autonomously, eliminating the need for manual human labeling while maintaining training effectiveness. This self-generated data cycle allows the system to scale without proportionally increasing human resource requirements.
Solution Approach 2:
Pseudo-labels serve as an intermediary between unlabeled data and the training process. Instead of requiring direct human labeling of all data, the system introduces pseudo-labels as a intermediate representation that bridges unlabeled data and the supervised learning framework, enabling automated data expansion while reducing manual intervention.
2Measurement precision
If manual labeling is performed to ensure data quality, then labeling accuracy is improved, but human error causes erroneous labeling that reduces model effectiveness
Solution Approach 1:
The neural network model performs self-labeling by generating pseudo-labels for unlabeled data through its own predictions. This eliminates human annotators from the labeling process entirely, removing the source of human error while maintaining consistent labeling standards based on the model's learned representations.
Solution Approach 2:
The system implements feedback mechanisms where the model's predictions on unlabeled data are evaluated against confidence thresholds and consistency criteria. Pseudo-labels that meet quality standards are fed back into the training process, while uncertain or inconsistent predictions are excluded or re-evaluated, ensuring high labeling accuracy through automated quality control.
3Ease of manufacture
If manually labeled data is used for training, then model training can be performed, but the difficulty of collecting recognized target images increases in vertical applications
Solution Approach 1:
The system enables self-service data generation by using the trained model to automatically create pseudo-labeled training data from available unlabeled images. In vertical applications like industrial vision or medicine, this allows the system to generate domain-specific training data without requiring manual collection and annotation of rare or specialized target images, significantly easing data acquisition while maintaining application-specific adaptability.
Solution Approach 2:
The system performs preliminary action by pre-training the model on available labeled data and then using this pre-trained model to generate pseudo-labels for expanding the training dataset. This preliminary training phase enables the model to understand domain-specific features before autonomously generating additional training examples, facilitating adaptation to vertical applications with limited initial labeled data.
Data Source
AI summary
An electronic device and a method for training a neural network model are provided. The method includes: obtaining a first neural network model and a first pseudo-labeled data; inputting the first pseudo-labeled data into the first neural network model to obtain a second pseudo-labeled data; determining whether a second pseudo-label corresponding to the second pseudo-labeled data matching a first pseudo-label corresponding to the first pseudo-labeled data; in response to the second pseudo-label matching the first pseudo-label, adding the second pseudo-labeled data to a pseudo-labeled dataset; and training the first neural network model according to the pseudo-labeled dataset.