mlsanityData to 14 Oct 2026

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

arXiv 1701.04128 ↗First posted 15 Jan 2017cs.CV cs.AI cs.LGcited by 553

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Cited by 36 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We study characteristics of receptive fields of units in deep convolutional networks. The receptive field size is a crucial issue in many visual tasks, as the output must respond to large enough areas in the image to capture information about large objects. We introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field. We analyze the effective receptive field in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it. This leads to suggestions for ways to address its tendency to be too small.

Key facts. Cited by 553 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2017.

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Citing papers per year: 2021: 93; 2022: 113; 2023: 100; 2024: 107; 2025: 77; 2026: 63.

553 papers.

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  18. 2024Mamba-ND: Selective State Space Modeling for Multi-Dimensional Datacited by 84
  19. 2022Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentationcited by 76
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