https://nalar.dev/tags/out-of-distribution-detection/ Recent content in Out-of-Distribution Detection on Nalar Hugo en-us Fri, 04 Sep 2026 00:00:00 +0700 https://nalar.dev/detect-out-of-distribution-inputs-before-trusting-a-model/ Fri, 04 Sep 2026 00:00:00 +0700 https://nalar.dev/detect-out-of-distribution-inputs-before-trusting-a-model/ <p>A model can produce a confident-looking prediction for an input that is unlike anything it was designed to handle. A product classifier trained on shoes, shirts, and bags still has to return some class when given a photo of a bicycle. The classifier&rsquo;s output layer does not automatically gain an <code>unknown</code> class just because the input is unfamiliar.</p> <p>This is the problem addressed by <strong>out-of-distribution detection</strong>, usually shortened to <strong>OOD detection</strong>. The goal is to recognize inputs that differ meaningfully from the data the model is expected to handle, before the application treats an ordinary model prediction as trustworthy.</p>