Few-Shot Generator for Knowledge Distillation Without Original Data
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Solution Overview
Problem
The challenge in knowledge distillation is accessing sufficient training samples for a student model due to privacy, proprietary, and availability concerns, especially when the teacher model's training samples are not shared, limiting the ability to deploy a smaller student model on less powerful devices.
Innovation Solution
A few-shot learning method is employed to train a generator that produces synthetic samples with similar data distribution to the teacher model's outputs, focusing on relevant features for the student model's specific task, and pivot-sample-based training is used to address domain differences in limited samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If knowledge distillation is performed using original training samples from the teacher model, then the student model can learn effectively, but privacy and proprietary concerns prevent access to these samples
Solution Approach 1:
The patent generates synthetic training samples that replicate the statistical properties and data distribution of the teacher model's original training samples. A generator network creates artificial samples that preserve the essential characteristics needed for knowledge distillation without requiring access to the actual proprietary or private training data, thus enabling effective student model training while respecting data accessibility constraints
Solution Approach 2:
The patent introduces a generator network as an intermediary component that bridges the gap between the teacher model and student model. This generator acts as a mediator by producing synthetic samples that capture the statistical essence of the original training data, allowing knowledge transfer without direct exposure to sensitive proprietary samples
2Productivity
If a student model is trained with limited training samples, then deployment on less powerful devices becomes feasible, but model accuracy deteriorates due to insufficient training data
Solution Approach 1:
The patent performs preliminary generation of synthetic training samples before the knowledge distillation process. By pre-generating a large corpus of synthetic samples that mimic the teacher model's training data distribution, the system ensures that the student model has sufficient training material available, thereby maintaining model accuracy while enabling deployment on resource-constrained devices
Solution Approach 2:
The patent transforms the training data parameter by changing from actual proprietary samples to synthetic generated samples. This parameter change allows the student model to receive adequate training data volume without requiring access to the original large-scale training set, thus maintaining accuracy while improving deployment feasibility
3Reliability
If synthetic samples are generated to match the teacher model's data distribution, then knowledge distillation effectiveness improves, but the complexity of training the generator increases
Solution Approach 1:
The patent implements a feedback mechanism where the generated synthetic samples are fed through the teacher model and the resulting statistics are used to guide further generation. This feedback loop allows the generator to iteratively improve the quality of synthetic samples by matching the statistical properties of the teacher model's training data, enhancing knowledge distillation effectiveness while managing training complexity through guided optimization
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for few-shot learning-based generator training are disclosed. An exemplary method may start with obtaining a teacher model and a plurality of training samples, as well as a generator for generating more training samples. After generating a plurality of additional training samples using the method may continue with feeding the plurality of generated additional training samples into the teacher model to obtain a plurality of first statistics; and feeding the plurality of training samples into the teacher model to obtain a plurality second statistics. Then the method further includes training the generator to minimize a distance between the plurality of first statistics and the plurality of second statistics.