mlsanityData to 14 Oct 2026

Going Deeper with Convolutions

arXiv 1409.4842 ↗First posted 17 Sep 2014cs.CVcited by 4,241

Not on the map. Why?

Cited by 85 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. This was achieved by a carefully crafted design that allows for increasing the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC 2014 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

Key facts. Cited by 4,241 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2014.

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Citing papers per year: 2018: 1; 2019: 2; 2020: 1; 2021: 1161; 2022: 928; 2023: 784; 2024: 678; 2025: 457; 2026: 229.

4,241 papers.

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  2. 2021Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervisioncited by 3,864
  3. 2022A ConvNet for the 2020scited by 3,579
  4. 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,962
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  10. 2021Perceiver: General Perception with Iterative Attentioncited by 1,104
  11. 2021Video Swin Transformercited by 1,091
  12. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
38 more
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  5. 2021Domain Generalization: A Surveycited by 764
  6. 2021Going deeper with Image Transformerscited by 764
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  10. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 712
  11. 2023Scaling Vision Transformers to 22 Billion Parameterscited by 694
  12. 2022EVA: Exploring the Limits of Masked Visual Representation Learning at Scalecited by 691
  13. 2021Perceiver IO: A General Architecture for Structured Inputs & Outputscited by 629
  14. 2021RegionCLIP: Region-based Language-Image Pretrainingcited by 616
  15. 2021LiT: Zero-Shot Transfer with Locked-image text Tuningcited by 577
  16. 2021CoAtNet: Marrying Convolution and Attention for All Data Sizescited by 569
  17. 2021How to train your ViT? Data, Augmentation, and Regularization in Vision Transformerscited by 569
  18. 2021Twins: Revisiting the Design of Spatial Attention in Vision Transformerscited by 565
  19. 2023VideoMAE V2: Scaling Video Masked Autoencoders with Dual Maskingcited by 528
  20. 2021Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networkscited by 527
  21. 2021CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windowscited by 527
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  23. 2021MViTv2: Improved Multiscale Vision Transformers for Classification and Detectioncited by 519
  24. 2022StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasetscited by 492
  25. 2021Less is More: ClipBERT for Video-and-Language Learning via Sparse Samplingcited by 484
  26. 2022InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutionscited by 482
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  28. 2021LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inferencecited by 475
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  30. 2022Masked Autoencoders As Spatiotemporal Learnerscited by 429
  31. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 425
  32. 2022DeiT III: Revenge of the ViTcited by 419
  33. 2021Dynamic Neural Networks: A Surveycited by 408
  34. 2021Early Convolutions Help Transformers See Bettercited by 396
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  38. 2021ResNet strikes back: An improved training procedure in timmcited by 368

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