Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Overview
Paper Summary
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.
Explain Like I'm Five
Imagine a CNN as having a blurry vision, focusing mostly on the center of what it "sees." This paper explores how that blurry spot (ERF) changes with different network designs.
Possible Conflicts of Interest
None identified
Identified Limitations
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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