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.

翻译：神经网络在自然语言处理、计算机视觉等多个领域实现了革命性突破，在各项任务中展现出卓越的准确率。然而，其对输入样本微小扰动的脆弱性在自动驾驶等安全关键型应用中提出了严峻挑战。现有方法如训练过程中引入数据失真，难以应对未知形式的损坏。本文提出一种创新的对抗对比学习框架，旨在同步增强神经网络对抗对抗攻击和常见损坏的鲁棒性。通过生成实例级对抗样本并优化对比损失，本方法可培育出既能抵御对抗扰动，又能在现实场景中保持稳健的表征。后续的对比学习进一步强化干净样本与其对抗副本之间的相似性，从而引导模型获得可同时抵抗对抗攻击和常见失真的表征能力。通过聚焦提升模型在对抗环境和现实条件下的表现性能，本方法致力于增强神经网络在安全关键型应用（如应对不可预测天气条件的自动驾驶车辆）中的鲁棒性。我们预期该框架将推动神经网络在复杂环境中可靠性的提升，促进其在任务关键型场景中的广泛应用。