mlsanityData to 7 Oct 2026

Striving for Simplicity: The All Convolutional Net

arXiv 1412.6806 ↗First posted 21 Dec 2014cs.LG cs.CV cs.NEcited by 983

Not on the map. Why?

Cited by 27 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks, questioning the necessity of different components in the pipeline. We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Following this finding -- and building on other recent work for finding simple network structures -- we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet). To analyze the network we introduce a new variant of the "deconvolution approach" for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches.

Key facts. Cited by 983 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2014.

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Citing papers per year: 2021: 231; 2022: 217; 2023: 206; 2024: 171; 2025: 111; 2026: 47.

983 papers.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2021Knowledge Neurons in Pretrained Transformerscited by 590
  3. 2021Causal Abstractions of Neural Networkscited by 401
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  6. 2023Towards Unbounded Machine Unlearningcited by 261
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  9. 2023Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretabilitycited by 194
  10. 2022CRAFT: Concept Recursive Activation FacTorization for Explainabilitycited by 177
  11. 2021Explainable artificial intelligence (XAI) in deep learning-based medical image analysiscited by 147
  12. 2021Parser-Free Virtual Try-on via Distilling Appearance Flowscited by 142
38 more
  1. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  2. 2023Boundary Unlearningcited by 132
  3. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  4. 2023Similarity of Neural Network Models: A Survey of Functional and Representational Measurescited by 128
  5. 2022Zero-Shot Machine Unlearningcited by 125
  6. 2021Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypescited by 120
  7. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  8. 2021Inducing Causal Structure for Interpretable Neural Networkscited by 107
  9. 2021R2RNet: Low-light Image Enhancement via Real-low to Real-normal Networkcited by 106
  10. 2021Guided Integrated Gradients: An Adaptive Path Method for Removing Noisecited by 102
  11. 2022A Consistent and Efficient Evaluation Strategy for Attribution Methodscited by 100
  12. 2021What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methodscited by 98
  13. 2022Adversarial Texture for Fooling Person Detectors in the Physical Worldcited by 96
  14. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 90
  15. 2022B-cos Networks: Alignment is All We Need for Interpretabilitycited by 88
  16. 2021Do Transformer Modifications Transfer Across Implementations and Applications?cited by 87
  17. 2021Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methodscited by 83
  18. 2021Explanations in Autonomous Driving: A Surveycited by 80
  19. 2022Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanationscited by 77
  20. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  21. 2021Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directionscited by 73
  22. 2022Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Datacited by 71
  23. 2021Deep One-Class Classification via Interpolated Gaussian Descriptorcited by 67
  24. 2021Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Futurecited by 67
  25. 2025Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Modelscited by 67
  26. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 66
  27. 2021Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysiscited by 65
  28. 2022A Rigorous Study of Integrated Gradients Method and Extensions to Internal Neuron Attributionscited by 64
  29. 2025Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignmentcited by 62
  30. 2021Applications of Deep Learning in Fundus Images: A Reviewcited by 59
  31. 2021Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretationscited by 59
  32. 2022Post hoc Explanations may be Ineffective for Detecting Unknown Spurious Correlationcited by 59
  33. 2021Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanationscited by 56
  34. 2021Convolutional Dynamic Alignment Networks for Interpretable Classificationscited by 54
  35. 2022CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behaviorcited by 54
  36. 2021Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAycited by 53
  37. 2021Shape or Texture: Understanding Discriminative Features in CNNscited by 52
  38. 2021Do Input Gradients Highlight Discriminative Features?cited by 52

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