mlsanityData to 7 Oct 2026

Green Learning: Introduction, Examples and Outlook

arXiv 2210.00965 ↗First posted 3 Oct 2022cs.LGcited by 30

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Abstract (arXiv)

Rapid advances in artificial intelligence (AI) in the last decade have largely been built upon the wide applications of deep learning (DL). However, the high carbon footprint yielded by larger and larger DL networks becomes a concern for sustainability. Furthermore, DL decision mechanism is somewhat obsecure and can only be verified by test data. Green learning (GL) has been proposed as an alternative paradigm to address these concerns. GL is characterized by low carbon footprints, small model sizes, low computational complexity, and logical transparency. It offers energy-effective solutions in cloud centers as well as mobile/edge devices. GL also provides a clear and logical decision-making process to gain people's trust. Several statistical tools have been developed to achieve this goal in recent years. They include subspace approximation, unsupervised and supervised representation learning, supervised discriminant feature selection, and feature space partitioning. We have seen a few successful GL examples with performance comparable with state-of-the-art DL solutions. This paper offers an introduction to GL, its demonstrated applications, and future outlook.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 126 of its 218 references that are in the map. First posted on arXiv 3 Oct 2022.

Builds on

126 of 218 references.

References · 126

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 13; 2024: 8; 2025: 4; 2026: 4.

30 papers.

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  1. 2024A Complete Survey on LLM-based AI Chatbotscited by 71
  2. 2024Perceptual Video Quality Assessment: A Surveycited by 43
  3. 2023An Overview on Language Models: Recent Developments and Outlookcited by 22
  4. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  5. 2023Knowledge Graph Embedding: An Overviewcited by 10
  6. 2023An Overview on Generative AI at Scale with Edge-Cloud Computingcited by 9
  7. 2022SALVE: Self-supervised Adaptive Low-light Video Enhancementcited by 4
  8. 2023S3I-PointHop: SO(3)-Invariant PointHop for 3D Point Cloud Classificationcited by 3
  9. 2023Knowledge Graph Embedding with 3D Compound Geometric Transformationscited by 3
  10. 2023Enhancing Edge Intelligence with Highly Discriminant LNT Featurescited by 3
  11. 2023AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relationscited by 2
  12. 2024GreenCOD: A Green Camouflaged Object Detection Methodcited by 2
18 more
  1. 2025GUSL-Dehaze: A Green U-Shaped Learning Approach to Image Dehazingcited by 2
  2. 2023PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Cloudscited by 1
  3. 2023Green Steganalyzer: A Green Learning Approach to Image Steganalysiscited by 1
  4. 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspectivecited by 1
  5. 2023LSR: A Light-Weight Super-Resolution Methodcited by 0
  6. 2023A Tiny Machine Learning Model for Point Cloud Object Classificationcited by 0
  7. 2023Unsupervised Green Object Tracker (GOT) without Offline Pre-trainingcited by 0
  8. 2024GWPT: A Green Word-Embedding-based POS Taggercited by 0
  9. 2024Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approachcited by 0
  10. 2024Word Embedding Dimension Reduction via Weakly-Supervised Feature Selectioncited by 0
  11. 2024Efficient Human-Object-Interaction (EHOI) Detection via Interaction Label Coding and Conditional Decisioncited by 0
  12. 2025Green Learning for STAR-RIS mmWave Systems with Implicit CSIcited by 0
  13. 2025LG-NuSegHop: A Local-to-Global Self-Supervised Pipeline For Nuclei Instance Segmentationcited by 0
  14. 2025Towards Sustainable Universal Deepfake Detection with Frequency-Domain Maskingcited by 0
  15. 2026Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classificationcited by 0
  16. 2026A Green Learning Approach to LDCT Image Restorationcited by 0
  17. 2026GreenPhase: A Green Learning Approach for Earthquake Phase Pickingcited by 0
  18. 2026MedSaab-US: A Backpropagation-Free Multi-Scale Wavelet-Saab Framework for Thyroid Nodule Segmentation in Ultrasound Imagescited by 0