mlsanityData to 7 Oct 2026

Example Perplexity

arXiv 2203.08813 ↗First posted 16 Mar 2022cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

Some examples are easier for humans to classify than others. The same should be true for deep neural networks (DNNs). We use the term example perplexity to refer to the level of difficulty of classifying an example. In this paper, we propose a method to measure the perplexity of an example and investigate what factors contribute to high example perplexity. The related codes and resources are available at https://github.com/vaynexie/Example-Perplexity.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 16 Mar 2022.

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6 of 10 references.

References · 6

  1. 2020Are we done with ImageNet?cited by 329
  2. 2020From ImageNet to Image Classification: Contextualizing Progress on Benchmarkscited by 90
  3. 2017Not All Pixels Are Equal: Difficulty-aware Semantic Segmentation via Deep Layer Cascadecited by 53
  4. 2017How hard can it be? Estimating the difficulty of visual search in an imagecited by 52
  5. 2020Difficulty-aware Glaucoma Classification with Multi-Rater Consensus Modelingcited by 17
  6. 2018Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracycited by 14

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