Neural networks have revolutionized various domains, exhibiting remarkable
accuracy in tasks like natural language processing and computer vision.
However, their vulnerability to slight alterations in input samples poses
challenges, particularly in safety-critical applications like autonomous
driving. Current approaches, such as introducing distortions during training,
fall short in addressing unforeseen corruptions. This paper proposes an
innovative adversarial contrastive learning framework to enhance neural network
robustness simultaneously against adversarial attacks and common corruptions.
By generating instance-wise adversarial examples and optimizing contrastive
loss, our method fosters representations that resist adversarial perturbations
and remain robust in real-world scenarios. Subsequent contrastive learning then
strengthens the similarity between clean samples and their adversarial
counterparts, fostering representations resistant to both adversarial attacks
and common distortions. By focusing on improving performance under adversarial
and real-world conditions, our approach aims to bolster the robustness of
neural networks in safety-critical applications, such as autonomous vehicles
navigating unpredictable weather conditions. We anticipate that this framework
will contribute to advancing the reliability of neural networks in challenging
environments, facilitating their widespread adoption in mission-critical
scenarios.

该研究提出了一种创新的对抗对比学习框架，通过生成实例级对抗样本和优化对比损失，增强了神经网络对抗攻击和常见扭曲的鲁棒性，同时通过对比学习增强了干净样本与对抗样本之间的相似性，从而使得表示具有对抗攻击和常见扭曲的抵抗力。通过改善对抗和实际应用条件下的性能，该方法旨在增强神经网络在安全关键应用中的鲁棒性，如自主车辆在不可预测的天气条件下行驶。我们预计这个框架将有助于提高神经网络在具有挑战性环境中的可靠性，促进其在任务关键场景中的广泛应用。