# Towards Robust Artificial Intelligence: Self-Supervised Learning Approach for Out-of-Distribution Detection

arXiv: https://arxiv.org/abs/2510.12713 · HTML: https://mlsanity.com/paper/2510.12713/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [A Survey on Contrastive Self-supervised Learning](https://mlsanity.com/paper/2011.00362/) (2020)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [SSD: A Unified Framework for Self-Supervised Outlier Detection](https://mlsanity.com/paper/2103.12051/) (2021)
- [A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection](https://mlsanity.com/paper/2106.09022/) (2021)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection](https://mlsanity.com/paper/2211.04208/) (2022)
- [Learning on Graphs with Out-of-Distribution Nodes](https://mlsanity.com/paper/2308.06714/) (2023)
- [Fine-Grained Segmentation Networks: Self-Supervised Segmentation for Improved Long-Term Visual Localization](https://mlsanity.com/paper/1908.06387/) (2019)
- [Gradient-based Novelty Detection Boosted by Self-supervised Binary Classification](https://mlsanity.com/paper/2112.09815/) (2021)
- [Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers](https://mlsanity.com/paper/2305.13849/) (2023)

## Abstract (arXiv)

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.