Adversarial Training Stabilizes Machine Learning Models
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Solution Overview
Problem
Existing machine learning models face challenges in effectively training with limited labeled data, often resulting in overfitting and knowledge forgetting due to aggressive tuning updates.
Innovation Solution
The proposed solution involves a two-stage process: pretraining using unlabeled data to obtain initial parameters, followed by a tuning stage that uses labeled samples with noise adjustment and adversarial regularization to adapt the parameters, while employing a proximal point mechanism to prevent overfitting and knowledge forgetting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If aggressive tuning updates are used to adapt parameters with limited labeled data, then the model can quickly adapt to the target task, but the model suffers from overfitting and knowledge forgetting
Solution Approach 1:
The patent implements dynamic learning rate scheduling that adjusts the tuning update strength based on training progress. The learning rate starts high to enable quick adaptation but decreases over time to prevent overfitting, allowing the system to transition from aggressive learning to stable refinement.
Solution Approach 2:
The patent modifies training parameters by introducing noise-adjusted versions of input data and dynamically adjusting the balance between original and noisy samples during tuning. This parameter transformation helps the model generalize better while adapting to the target task.
2Measurement precision
If strong tuning is applied to achieve good performance on labeled data, then the model achieves high accuracy on training samples, but the model forgets previously learned knowledge from pretraining
Solution Approach 1:
The patent applies preprocessing to the training data by adding controlled noise and creating augmented versions before the tuning stage. This preliminary transformation prepares the data to prevent the model from memorizing exact training examples, thereby preserving knowledge from pretraining while still achieving good training accuracy.
Solution Approach 2:
The patent introduces noise-adjusted training samples as an intermediary between the original training data and the model. These modified samples act as a buffer that allows the model to learn from training data without directly memorizing it, thus preventing knowledge forgetting.
3Reliability
If more labeled training data is collected to improve model performance, then the model generalization improves, but the data collection cost and time increase
Solution Approach 1:
The patent performs pretraining on large unlabeled datasets before the tuning stage. This preliminary action allows the model to learn general patterns and representations that transfer to the target task, reducing the amount of labeled data needed for effective tuning and thereby reducing data collection time.
Solution Approach 2:
The patent employs data augmentation techniques where the model generates its own training variations through noise addition and transformations. This self-service approach effectively increases the training data availability without requiring external data collection efforts.
Data Source
AI summary
This document relates to training of machine learning models such as neural networks. One example method involves providing a machine learning model having one or more layers and associated parameters and performing a pretraining stage on the parameters of the machine learning model to obtain pretrained parameters. The example method also involves performing a tuning stage on the machine learning model by using labeled training samples to tune the pretrained parameters. The tuning stage can include performing noise adjustment of the labeled training examples to obtain noise-adjusted training samples. The tuning stage can also include adjusting the pretrained parameters based at least on the labeled training examples and the noise-adjusted training examples to obtain adapted parameters. The example method can also include outputting a tuned machine learning model having the adapted parameters.