# CAT: Customized Adversarial Training for Improved Robustness

arXiv: https://arxiv.org/abs/2002.06789 · HTML: https://mlsanity.com/paper/2002.06789/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2020.

## Built on by (most cited first, 30 of 61)

- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Cross-Entropy Loss Functions: Theoretical Analysis and Applications](https://mlsanity.com/paper/2304.07288/) (2023)
- [Exploring Memorization in Adversarial Training](https://mlsanity.com/paper/2106.01606/) (2021)
- [CFA: Class-wise Calibrated Fair Adversarial Training](https://mlsanity.com/paper/2303.14460/) (2023)
- [Adversarial Robustness with Semi-Infinite Constrained Learning](https://mlsanity.com/paper/2110.15767/) (2021)
- [Fast Adversarial Training with Adaptive Step Size](https://mlsanity.com/paper/2206.02417/) (2022)
- [MeanSparse: Post-Training Robustness Enhancement Through Mean-Centered Feature Sparsification](https://mlsanity.com/paper/2406.05927/) (2024)
- [Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing](https://mlsanity.com/paper/2301.12554/) (2023)
- [SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation](https://mlsanity.com/paper/2102.03716/) (2021)
- [MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers](https://mlsanity.com/paper/2402.02263/) (2024)
- [Why adversarial training can hurt robust accuracy](https://mlsanity.com/paper/2203.02006/) (2022)
- [Improving Robustness by Enhancing Weak Subnets](https://mlsanity.com/paper/2201.12765/) (2022)
- [Holistic Adversarial Robustness of Deep Learning Models](https://mlsanity.com/paper/2202.07201/) (2022)
- [AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning](https://mlsanity.com/paper/2108.06017/) (2021)
- [Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries](https://mlsanity.com/paper/2103.14824/) (2021)
- [A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities](https://mlsanity.com/paper/2210.08906/) (2022)
- [Guided Interpolation for Adversarial Training](https://mlsanity.com/paper/2102.07327/) (2021)
- [NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels](https://mlsanity.com/paper/2105.14676/) (2021)
- [Consistent Non-Parametric Methods for Maximizing Robustness](https://mlsanity.com/paper/2102.09086/) (2021)
- [Domain Invariant Adversarial Learning](https://mlsanity.com/paper/2104.00322/) (2021)
- [Raising the Bar for Certified Adversarial Robustness with Diffusion Models](https://mlsanity.com/paper/2305.10388/) (2023)
- [Adversarial Learning with Cost-Sensitive Classes](https://mlsanity.com/paper/2101.12372/) (2021)
- [Provable Unrestricted Adversarial Training without Compromise with Generalizability](https://mlsanity.com/paper/2301.09069/) (2023)
- [Combining Adversaries with Anti-adversaries in Training](https://mlsanity.com/paper/2304.12550/) (2023)
- [Insta-RS: Instance-wise Randomized Smoothing for Improved Robustness and Accuracy](https://mlsanity.com/paper/2103.04436/) (2021)
- [Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing](https://mlsanity.com/paper/2303.14077/) (2023)
- [Splitting the Difference on Adversarial Training](https://mlsanity.com/paper/2310.02480/) (2023)
- [Dynamic Guidance Adversarial Distillation with Enhanced Teacher Knowledge](https://mlsanity.com/paper/2409.01627/) (2024)
- [Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection](https://mlsanity.com/paper/2206.01736/) (2022)
- [A3T: Accuracy Aware Adversarial Training](https://mlsanity.com/paper/2211.16316/) (2022)

## Abstract (arXiv)

Adversarial training has become one of the most effective methods for improving robustness of neural networks. However, it often suffers from poor generalization on both clean and perturbed data. In this paper, we propose a new algorithm, named Customized Adversarial Training (CAT), which adaptively customizes the perturbation level and the corresponding label for each training sample in adversarial training. We show that the proposed algorithm achieves better clean and robust accuracy than previous adversarial training methods through extensive experiments.