# Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness

arXiv: https://arxiv.org/abs/2207.10899 · HTML: https://mlsanity.com/paper/2207.10899/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 68 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 30 of 63)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning](https://mlsanity.com/paper/2105.04906/) (2021)
- [VL-BERT: Pre-training of Generic Visual-Linguistic Representations](https://mlsanity.com/paper/1908.08530/) (2019)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)

## Built on by (most cited first, 20 of 20)

- [Faster Segment Anything: Towards Lightweight SAM for Mobile Applications](https://mlsanity.com/paper/2306.14289/) (2023)
- [Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models](https://mlsanity.com/paper/2402.12336/) (2024)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://mlsanity.com/paper/2304.06488/) (2023)
- [Few-Shot Adversarial Prompt Learning on Vision-Language Models](https://mlsanity.com/paper/2403.14774/) (2024)
- [Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples](https://mlsanity.com/paper/2403.10801/) (2024)
- [Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning](https://mlsanity.com/paper/2303.01289/) (2023)
- [MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence](https://mlsanity.com/paper/2304.01950/) (2023)
- [Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection](https://mlsanity.com/paper/2302.03857/) (2023)
- [Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularization](https://mlsanity.com/paper/2305.00374/) (2023)
- [AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework](https://mlsanity.com/paper/2310.01818/) (2023)
- [Improving Adversarial Robustness by Contrastive Guided Diffusion Process](https://mlsanity.com/paper/2210.09643/) (2022)
- [Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks](https://mlsanity.com/paper/2407.12588/) (2024)
- [Distortion-Disentangled Contrastive Learning](https://mlsanity.com/paper/2303.05066/) (2023)
- [Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning](https://mlsanity.com/paper/2606.10613/) (2026)
- [Adversarial Robustness of Discriminative Self-Supervised Learning in Vision](https://mlsanity.com/paper/2503.06361/) (2025)
- [DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data](https://mlsanity.com/paper/2307.04075/) (2023)
- [Towards Understanding Dual BN In Hybrid Adversarial Training](https://mlsanity.com/paper/2403.19150/) (2024)
- [ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations](https://mlsanity.com/paper/2406.05796/) (2024)
- [LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders](https://mlsanity.com/paper/2505.18884/) (2025)
- [Model-Guided Dual-Role Alignment for High-Fidelity Open-Domain Video-to-Audio Generation](https://mlsanity.com/paper/2510.24103/) (2025)

## Abstract (arXiv)

Adversarial training (AT) for robust representation learning and self-supervised learning (SSL) for unsupervised representation learning are two active research fields. Integrating AT into SSL, multiple prior works have accomplished a highly significant yet challenging task: learning robust representation without labels. A widely used framework is adversarial contrastive learning which couples AT and SSL, and thus constitute a very complex optimization problem. Inspired by the divide-and-conquer philosophy, we conjecture that it might be simplified as well as improved by solving two sub-problems: non-robust SSL and pseudo-supervised AT. This motivation shifts the focus of the task from seeking an optimal integrating strategy for a coupled problem to finding sub-solutions for sub-problems. With this said, this work discards prior practices of directly introducing AT to SSL frameworks and proposed a two-stage framework termed Decoupled Adversarial Contrastive Learning (DeACL). Extensive experimental results demonstrate that our DeACL achieves SOTA self-supervised adversarial robustness while significantly reducing the training time, which validates its effectiveness and efficiency. Moreover, our DeACL constitutes a more explainable solution, and its success also bridges the gap with semi-supervised AT for exploiting unlabeled samples for robust representation learning. The code is publicly accessible at https://github.com/pantheon5100/DeACL.