无需标注数据，用自监督学习提升模型对异常样本的识别能力。
Towards Robust Artificial Intelligence: Self-Supervised Learning Approach for Out-of-Distribution Detection
- 利用自监督学习从无标签数据中提取有效特征
- 结合图论方法实现异常样本高效检测，AUROC达0.99
- 适合自动驾驶等高安全要求场景的鲁棒性增强
AI系统的鲁棒性指其在各种条件下（包括分布外样本、对抗攻击和环境变化）保持可靠准确性能的能力。这在自动驾驶、交通或医疗等安全关键系统中尤为重要。本文提出一种无需标注数据的分布外（OOD）检测方法，通过自监督学习从无标签数据中学习有用表征，并结合图论技术，更高效地识别和分类分布外样本。相比现有最先进方法，该方法在测试集上达到AUROC = 0.99。
原文摘要 · Abstract (English)
Robustness in AI systems refers to their ability to maintain reliable and accurate performance under various conditions, including out-of-distribution (OOD) samples, adversarial attacks, and environmental changes. This is crucial in safety-critical systems, such as autonomous vehicles, transportation, or healthcare, where malfunctions could have severe consequences. This paper proposes an approach to improve OOD detection without the need of labeled data, thereby increasing the AI systems' robustness. The proposed approach leverages the principles of self-supervised learning, allowing the model to learn useful representations from unlabeled data. Combined with graph-theoretical techniques, this enables the more efficient identification and categorization of OOD samples. Compared to existing state-of-the-art methods, this approach achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) = 0.99.
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