mlsanityData to 14 Oct 2026

When in Doubt: Improving Classification Performance with Alternating Normalization

arXiv 2109.13449 ↗First posted 28 Sep 2021cs.LG cs.CL cs.CVcited by 1

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Abstract (arXiv)

We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification. CAN improves classification accuracy for challenging examples by re-adjusting their predicted class probability distribution using the predicted class distributions of high-confidence validation examples. CAN is easily applicable to any probabilistic classifier, with minimal computation overhead. We analyze the properties of CAN using simulated experiments, and empirically demonstrate its effectiveness across a diverse set of classification tasks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

16 of 30 references.

References · 16

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4 more
  1. 2018Pragmatically Informative Image Captioning with Character-Level Inferencecited by 42
  2. 2019Learning to Denoise Distantly-Labeled Data for Entity Typingcited by 24
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