Student Model Distillation via Arbitrary Label Generation
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Solution Overview
Problem
Existing distillation learning methods using unknown data without true labels can result in biased predictions due to class imbalance, preventing effective knowledge transfer from a teacher model to a student model.
Innovation Solution
An information processing device and method that includes a label distribution determination unit to output an arbitrary label for unknown data, a data generation unit to generate new data based on the arbitrary label and unknown data, and a distillation learning unit to perform distillation learning using the teacher model and generated data, thereby addressing class imbalance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distillation learning uses unknown data without true labels, then learning data privacy and copyright protection is improved, but class imbalance causes biased predictions and prevents effective knowledge transfer
Solution Approach 1:
The patent introduces an intermediary component (label distribution determination unit) that mediates between the unknown data and the student model. This unit determines the label distribution based on the teacher model's predictions and uses it to generate weighted average labels, thereby eliminating the need to store or access true labels while maintaining prediction accuracy through the distillation process
Solution Approach 2:
The patent changes the parameter representation from true labels (discrete class identifiers) to weighted average labels (continuous probability distributions). This transformation allows the system to work with unknown data without true labels by using the teacher model's predicted label distributions as soft targets, thus resolving the class imbalance issue while protecting data privacy
2Measurement precision
If distillation learning uses weighted average labels from teacher model predictions, then student model learning accuracy is improved, but the original learning data must be retained which increases storage requirements
Solution Approach 1:
The patent extracts only the essential information (label distribution patterns) from the teacher model's predictions without requiring the original learning data. The label distribution determination unit extracts the probabilistic structure of labels from teacher predictions on unknown data, separating this extracted information from the need to store the original training dataset
Solution Approach 2:
Instead of storing and reusing the original learning data, the system creates a copy of the label distribution information through the teacher model's predictions on unknown data. This copied label distribution serves as a surrogate for the true labels, enabling distillation learning without accessing or storing the original training data
3Reliability
If unknown data distribution differs from teacher model learning data distribution, then data privacy is maintained, but the teacher model's predictions become biased and distillation learning fails
Solution Approach 1:
The patent introduces dynamics into the label determination process by using the teacher model's predictions on the specific unknown data to determine label distributions adaptively. Rather than relying on static training data distributions, the system dynamically adjusts the label distributions based on the teacher model's predictions for the given unknown data, allowing adaptation to different data distributions while maintaining privacy
Solution Approach 2:
The system changes the parameter basis from fixed training data statistics to dynamic teacher model predictions. By using the teacher model's predicted label distributions as the basis for distillation targets, the system adapts to different unknown data distributions without requiring the original training data, thus maintaining both privacy and performance
Data Source
AI summary
The information processing device performs distillation learning of a student model using unknown data which a teacher model has not learned. The label distribution determination unit outputs an arbitrary label for the unknown data. The data generation unit outputs new generated data using an arbitrary label and unknown data as inputs. The distillation learning part performs distillation learning of the student model using the teacher model and using the generated data as an input.