Neural Network Label Generator for Overfitting Reduction
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Solution Overview
Problem
Neural networks often suffer from overfitting due to limited labeled data, leading to poor performance on unseen data, and existing solutions like temporal ensembling are inefficient, especially in online learning scenarios.
Innovation Solution
A method involving a neural network system with an original network and a label generator, where the label generator updates its weights based on the original network's weights, generating additional labels to improve training efficiency and reduce overfitting by minimizing a combination of classification and consistency costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the neural network is trained with more labeled data to mitigate overfitting, then the generalization performance is improved, but the training cost and time increase due to the laborious and expensive labeling process
Solution Approach 1:
The neural network generates its own training labels by processing unlabeled data through itself, eliminating the need for external human labeling. The network uses its current predictions as pseudo-labels for continued training, making the system self-sufficient and avoiding the time-consuming manual annotation process while maintaining generalization performance
Solution Approach 2:
The system performs preliminary processing of unlabeled data by generating predicted labels before formal training occurs. These pre-generated labels are then used to augment the training set, allowing the network to learn from additional data without waiting for manual labeling, thus reducing training time while improving generalization
2Measurement precision
If the neural network complexity is increased to model complicated relationships, then the training accuracy on labeled data is improved, but the overfitting problem worsens
Solution Approach 1:
The system uses a form of dropout where randomly selected neurons are ignored during training, effectively training a partial network. This prevents any single complex pathway from dominating and reduces overfitting while maintaining the capacity to learn complicated relationships when needed
Solution Approach 2:
The system dynamically changes the effective network complexity during training by randomly muting neurons. This transforms the fixed complex architecture into a variable complexity system that adapts during training, preventing overfitting while preserving the ability to model complex relationships
3Measurement precision
If temporal ensembling is used to reduce variance and bias of generated labels, then the label quality is improved, but the training efficiency deteriorates due to updates only once per epoch
Solution Approach 1:
Instead of updating labels once per epoch, the system continuously updates pseudo-labels for unlabeled data at every training step. This continuous refinement of labels maintains high label quality through the ensembling effect while dramatically improving training efficiency by utilizing all available data throughout the entire training process
Data Source
AI summary
Disclosed is a computer-implemented method for training a neural network system including an original neural network and a label generator. The method is based on an idea that the neural network system is trained by a sequence of training steps where at each training step at least one of a plurality of operations is performed and each of the operations gets performed at least once during training of the neural network system. Also disclosed are a neural network system and a computer program product.