mlsanityData to 14 Oct 2026

Thinkback: Task-SpecificOut-of-Distribution Detection

arXiv 2107.06668 ↗First posted 13 Jul 2021cs.LG cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

The increased success of Deep Learning (DL) has recently sparked large-scale deployment of DL models in many diverse industry segments. Yet, a crucial weakness of supervised model is the inherent difficulty in handling out-of-distribution samples, i.e., samples belonging to classes that were not presented to the model at training time. We propose in this paper a novel way to formulate the out-of-distribution detection problem, tailored for DL models. Our method does not require fine tuning process on training data, yet is significantly more accurate than the state of the art for out-of-distribution detection.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 13 Jul 2021.

Builds on

18 of 28 references.

References · 18

  1. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  2. 2016Wide Residual Networkscited by 3,070
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  4. 2013Describing Textures in the Wildcited by 2,125
  5. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  6. 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
  7. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  8. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  9. 2015Towards Open Set Deep Networkscited by 538
  10. 2015TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Trackingcited by 165
  11. 2017Generative OpenMax for Multi-Class Open Set Classificationcited by 157
  12. 2018Classification-Reconstruction Learning for Open-Set Recognitioncited by 146
6 more
  1. 2015The Extreme Value Machinecited by 78
  2. 2018Clustering with Deep Learning: Taxonomy and New Methodscited by 46
  3. 2018Learning a Neural-network-based Representation for Open Set Recognitioncited by 40
  4. 2020A Benchmark of Medical Out of Distribution Detectioncited by 40
  5. 2020Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Updatecited by 16
  6. 2020One-vs-Rest Network-based Deep Probability Model for Open Set Recognitioncited by 7

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023DataZoo: Streamlining Traffic Classification Experimentscited by 2