Distribution shift
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Improving Reliability of Deep Learning System Under Distribution Shift Via Feature-Space Out-of-Distribution Detection
Abstract: Deep learning systems achieve strong predictive performance under independently and identically distributed conditions; however, their reliability can decline significantly when exposed to distribution shifts during deployment. This decline is often accompanied by overconfident predictions, which can introduce serious risks in safety-critical applications. In this work, we explore whether explicitly modeling the structure of the latent feature space can improve reliability through more effective out-ofdistribution detection. A statistically grounded framework based …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 66–76 Read article