arXiv Machine Learning July 15, 2026 By Kun Fang, Qinghua Tao, Mingzhen He, Kexin Lv, Runze Yang, Haibo Hu, Xiaolin Huang, Jie Yang, Longbing Cao

Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selection and Approximation

Read the original on arXiv Machine Learning →

arXiv:2505. 15284v2 Announce Type: replace Abstract: Out-of-Distribution (OoD) detection is vital for the reliability of deep neural networks, the key of which lies in effectively characterizing the disparities between OoD and In-Distribution (InD) data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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phepy: Visual benchmarks and improvements for out-of-distribution detectors

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