Manifold Regularized Deep Neural Networks using Adversarial Examples

Understand

Learning meaningful representations using deep neural networks involves designing efficient training schemes and well-structured networks.

• Currently, the method of stochastic gradient descent that has a momentum with dropout is one of the most popular training protocols.
• Based on that, more advanced methods (i.e., Maxout and Batch Normalization) have been proposed in recent years, but most still suffer from performance degradation caused by small perturbations, also known as adversarial examples.
• To address this issue, we propose manifold regularized networks (MRnet) that utilize a novel training objective function that minimizes the difference between multi-layer embedding results of samples and those adversarial.