mlsanityData to 14 Oct 2026

dlordinal: a Python package for deep ordinal classification

arXiv 2407.17163 ↗First posted 24 Jul 2024cs.LGcited by 4

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Abstract (arXiv)

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://github.com/ayrna/dlordinal.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 40 references that are in the map. First posted on arXiv 24 Jul 2024.

Builds on

6 of 40 references.

References · 6

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2015Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labelscited by 12
  3. 2017Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanationcited by 11
  4. 2019A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysiscited by 11
  5. 2023Unimodal Distributions for Ordinal Regressioncited by 9
  6. 2023A Dictionary-based approach to Time Series Ordinal Classificationcited by 1

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Cited by year (2026 to 14 Oct)

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

4 papers.