mlsanityData to 14 Oct 2026

CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue

arXiv 2108.06024 ↗First posted 13 Aug 2021cs.CVcited by 14

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Abstract (arXiv)

Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks. The key to resolve the OOD overconfidence issue inherently is to build a subset of OOD samples and then suppress predictions on them. This paper proposes the Chamfer OOD examples (CODEs), whose distribution is close to that of in-distribution samples, and thus could be utilized to alleviate the OOD overconfidence issue effectively by suppressing predictions on them. To obtain CODEs, we first generate seed OOD examples via slicing&splicing operations on in-distribution samples from different categories, and then feed them to the Chamfer generative adversarial network for distribution transformation, without accessing to any extra data. Training with suppressing predictions on CODEs is validated to alleviate the OOD overconfidence issue largely without hurting classification accuracy, and outperform the state-of-the-art methods. Besides, we demonstrate CODEs are useful for improving OOD detection and classification.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 13 Aug 2021.

Builds on

33 of 52 references.

References · 33

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 4; 2025: 3; 2026: 1.

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  1. 2022Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognitioncited by 44
  2. 2022Isolation and Impartial Aggregation: A Paradigm of Incremental Learning without Interferencecited by 44
  3. 2022Novel Class Discovery without Forgettingcited by 39
  4. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 26
  5. 2023EAT: Towards Long-Tailed Out-of-Distribution Detectioncited by 16
  6. 2024Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Networkcited by 15
  7. 2024Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learningcited by 9
  8. 2025Mitigating Hallucinations in Large Vision-Language Models by Adaptively Constraining Information Flowcited by 9
  9. 2024CLIP-driven Outliers Synthesis for few-shot OOD detectioncited by 6
  10. 2025EOOD: Entropy-based Out-of-distribution Detectioncited by 5
  11. 2023Image Background Serves as Good Proxy for Out-of-distribution Datacited by 3
  12. 2025NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevancecited by 3
2 more
  1. 2023Free Lunch for Generating Effective Outlier Supervisioncited by 0
  2. 2026Optimal Transport-Induced Samples against Out-of-Distribution Overconfidencecited by 0