Adversarial Pretraining for Machine Learning Robustness
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional machine learning models are susceptible to adversarial attacks and tend to compromise generalization performance when trained using adversarial techniques.
Innovation Solution
The implementation of virtual adversarial pretraining for one or more mapping layers of a machine learning model, followed by tuning with a task-specific layer using supervised learning, to enhance both robustness and generalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training techniques are used to improve robustness, then the model becomes resistant to adversarial attacks, but generalization performance deteriorates
Solution Approach 1:
The training process is divided into two distinct stages: pretraining stage where adversarial examples are generated and used to train mapping layers, and fine-tuning stage where the model is trained on clean data for specific tasks. This segmentation allows the model to learn robust feature representations without the negative effects of adversarial training on task-specific performance
Solution Approach 2:
Adversarial training is performed as a preliminary action during the pretraining stage before the model is fine-tuned for specific tasks. By preparing the mapping layers with robust feature extraction capabilities in advance, the model gains resistance to adversarial attacks while maintaining the ability to generalize well on clean task-specific data during the subsequent fine-tuning stage
Data Source
AI summary
This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.