mlsanityData to 14 Oct 2026

ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks

arXiv 2503.21397 ↗First posted 27 Mar 2025cs.LG cs.CV stat.MLcited by 4

Not on the map. Why?

Abstract (arXiv)

Out-of-distribution (OOD) detection in deep learning has traditionally been framed as a binary task, where samples are either classified as belonging to the known classes or marked as OOD, with little attention given to the semantic relationships between OOD samples and the in-distribution (ID) classes. We propose a framework for detecting and classifying OOD samples in a given class hierarchy. Specifically, we aim to predict OOD data to their correct internal nodes of the class hierarchy, whereas the known ID classes should be predicted as their corresponding leaf nodes. Our approach leverages the class hierarchy to create a probabilistic model and we implement this model by using networks trained for ID classification at multiple hierarchy depths. We conduct experiments on three datasets with predefined class hierarchies and show the effectiveness of our method. Our code is available at https://github.com/walline/prohoc.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

22 of 31 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  4. 2013Fine-Grained Visual Classification of Aircraftcited by 1,726
  5. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  6. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  7. 2020Energy-based Out-of-distribution Detectioncited by 1,299
  8. 2017Deeper, Broader and Artier Domain Generalizationcited by 986
  9. 2015Towards Open Set Deep Networkscited by 538
  10. 2018Meta-Learning for Semi-Supervised Few-Shot Classificationcited by 456
  11. 2019Likelihood Ratios for Out-of-Distribution Detectioncited by 453
  12. 2022ViM: Out-Of-Distribution with Virtual-logit Matchingcited by 405
10 more
  1. 2021Open-Set Recognition: a Good Closed-Set Classifier is All You Need?cited by 353
  2. 2023OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detectioncited by 179
  3. 2019Making Better Mistakes: Leveraging Class Hierarchies with Deep Networkscited by 113
  4. 2023NECO: NEural Collapse Based Out-of-distribution detectioncited by 42
  5. 2022Learning Hierarchy Aware Features for Reducing Mistake Severitycited by 26
  6. 2021No Cost Likelihood Manipulation at Test Time for Making Better Mistakes in Deep Networkscited by 21
  7. 2023Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for Reducing Mistake Severitycited by 13
  8. 2022Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classificationcited by 10
  9. 2023Test-Time Amendment with a Coarse Classifier for Fine-Grained Classificationcited by 5
  10. 2024COOD: Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classificationcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025A Multi-dimensional Semantic Surprise Framework Based on Low-Entropy Semantic Manifolds for Fine-Grained Out-of-Distribution Detectioncited by 3
  2. 2026Semi-Supervised Hierarchical Open-Set Classificationcited by 1
  3. 2026Deep learning-based hierarchical insect classification using camera trap imagerycited by 0
  4. 2026Subtype Robustness Is Not Just Accuracy: Calibration Under Unseen Subtype Shiftcited by 0