arXiv:2603.08413v2 Announce Type: replace Abstract: Deep neural networks for image classification often exhibit overconfidence on out-of-distribution (OOD) samples. To address this, we introduce Geometrically Constrained Outlier Synthesis (GCOS), a training-time regularization framework aimed at improving OOD robustness during inference. GCOS addresses a limitation of prior synthesis methods by generating virtual outliers in the hidden feature space that respect the learned manifold structure of in-distribution (ID) data. The synthesis proceeds in two stages: (i) a dominant-variance subspace e
The proliferation of deep neural networks in real-world applications highlights the urgent need for robust AI that performs reliably on novel data, making ongoing research into OOD detection crucial.
Improving Out-of-Distribution (OOD) robustness is critical for deploying reliable and safe AI systems, particularly in sensitive applications where unexpected inputs could lead to failures.
The GCOS framework offers a novel approach to training-time regularization by synthesizing virtual outliers that maintain geometric coherence within learned feature spaces, potentially leading to more robust models.
- · AI developers
- · Industries relying on AI for critical applications
- · Researchers in AI safety and robustness
- · AI models without OOD robustness
- · Systems vulnerable to adversarial attacks
- · Naive synthetic data generation methods
Deep neural networks become more reliable in real-world, unpredictable environments.
Increased trust and adoption of AI in high-stakes domains, such as autonomous vehicles or medical diagnostics.
New AI safety standards and regulatory frameworks emerge, demanding specific OOD robustness benchmarks and mitigations.
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Read at arXiv cs.LG