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An Introduction to Convolutional Neural Networks

arXiv 1511.08458 ↗First posted 26 Nov 2015cs.NE cs.CV cs.LGcited by 514

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Abstract (arXiv)

The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These biologically inspired computational models are able to far exceed the performance of previous forms of artificial intelligence in common machine learning tasks. One of the most impressive forms of ANN architecture is that of the Convolutional Neural Network (CNN). CNNs are primarily used to solve difficult image-driven pattern recognition tasks and with their precise yet simple architecture, offers a simplified method of getting started with ANNs. This document provides a brief introduction to CNNs, discussing recently published papers and newly formed techniques in developing these brilliantly fantastic image recognition models. This introduction assumes you are familiar with the fundamentals of ANNs and machine learning.

Key facts. Cited by 514 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Nov 2015.

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Citing papers per year: 2021: 41; 2022: 49; 2023: 84; 2024: 127; 2025: 146; 2026: 67.

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  8. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 52
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  10. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
  11. 2025A Survey of State of the Art Large Vision Language Models: Alignment, Benchmark, Evaluations and Challengescited by 47
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38 more
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  4. 2024GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networkscited by 32
  5. 2024InternalInspector I2: Robust Confidence Estimation in LLMs through Internal Statescited by 30
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  7. 2024A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generationcited by 29
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  12. 2023Complex Organ Mask Guided Radiology Report Generationcited by 22
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  17. 2023Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guaranteescited by 19
  18. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
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  25. 2023Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challengescited by 14
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  28. 2023GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformercited by 13
  29. 2023A Review of Driver Gaze Estimation and Application in Gaze Behavior Understandingcited by 12
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  31. 2025RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguitycited by 12
  32. 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Visioncited by 11
  33. 2023Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensingcited by 11
  34. 2024Approximating Numerical Fluxes Using Fourier Neural Operators for Hyperbolic Conservation Lawscited by 11
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  38. 2021Increasing the Confidence of Deep Neural Networks by Coverage Analysiscited by 9

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