# Feedback Learning for Improving the Robustness of Neural Networks

arXiv: https://arxiv.org/abs/1909.05443 · HTML: https://mlsanity.com/paper/1909.05443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency](https://mlsanity.com/paper/2109.05223/) (2021)
- [A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness](https://mlsanity.com/paper/2110.12308/) (2021)

## Abstract (arXiv)

Recent research studies revealed that neural networks are vulnerable to adversarial attacks. State-of-the-art defensive techniques add various adversarial examples in training to improve models' adversarial robustness. However, these methods are not universal and can't defend unknown or non-adversarial evasion attacks. In this paper, we analyze the model robustness in the decision space. A feedback learning method is then proposed, to understand how well a model learns and to facilitate the retraining process of remedying the defects. The evaluations according to a set of distance-based criteria show that our method can significantly improve models' accuracy and robustness against different types of evasion attacks. Moreover, we observe the existence of inter-class inequality and propose to compensate it by changing the proportions of examples generated in different classes.