Understanding the Effective Receptive Field in Deep Convolutional Neural Networks

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Conflicts of Interest

Identified Weaknesses

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Paper Summary

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Deep CNNs Don't "See" as Much as We Thought: The Effective Receptive Field is Smaller Than Expected!

This paper introduces the concept of an "effective receptive field" (ERF) in deep convolutional neural networks (CNNs), showing it's smaller than the theoretical receptive field and follows a Gaussian distribution. The authors analyze how ERF size is affected by factors like network depth, non-linear activations, and skip connections, and they suggest ways to increase ERF size, such as modified initialization and architectural changes.

Possible Conflicts of Interest

None identified

Identified Weaknesses

Theoretical Focus

While the paper provides some empirical validation, its primary focus is theoretical. More extensive experiments on various CNN architectures and datasets would strengthen the conclusions.

Simplifications in Analysis

The analysis makes certain simplifying assumptions, like independence between gradients and weights, which may not always hold in practice, especially with non-linear activations.

Limited Scope of Architectural Changes

The architectural changes proposed are relatively limited. Exploring more diverse architectural modifications to expand ERF size would be beneficial.

Rating Explanation

This paper presents a novel and insightful theoretical analysis of effective receptive fields in CNNs. The concept of ERF is important for understanding and designing CNNs. While primarily theoretical and relying on some simplifications, the work offers valuable insights and opens avenues for future research. Therefore, it deserves a rating of 4.

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