Adversarial Margin Maximization Networks.

Area of Science:

• Machine Learning
• Computer Vision
• Deep Learning

Background:

• Deep neural networks (DNNs) demonstrate remarkable success but require vast datasets and struggle with generalization.
• DNNs are vulnerable to adversarial examples, which are perceptually similar inputs designed to fool models.
• Current models exhibit poor generalization to unseen data with specific distortions.

Purpose of the Study:

• To comprehensively analyze the generalization ability of DNNs.
• To improve DNN generalization from a geometric perspective.
• To introduce a novel regularization technique for enhanced model robustness.

Main Methods:

• Propose adversarial margin maximization (AMM), a learning-based regularization method.
• Utilize adversarial perturbation as a proxy to encourage large margins in the input space.
• Employ a differentiable formulation of perturbation for end-to-end training via back-propagation.

Main Results:

• Demonstrate the superiority of AMM over state-of-the-art methods.
• Achieve improved generalization performance across various datasets (MNIST, CIFAR-10/100, SVHN, ImageNet).
• Validate effectiveness across different DNN architectures.

Conclusions:

• AMM significantly enhances the generalization ability of DNNs.
• The geometric approach offers a promising direction for robust deep learning.
• Publicly available code and models facilitate reproducibility and further research.