CNN Receptive Field | Deep Learning Animated 

In this video, we explore the critical concept of the receptive field in convolutional neural networks (CNNs). Understanding the receptive field is essential for grasping how CNNs process images and detect patterns. We will explain both the theoretical and effective receptive fields, highlighting how they influence network performance and design.
We start by defining the receptive field and its importance in CNNs, demonstrating how it grows with each convolutional layer. We'll use examples to compute the receptive field for different network configurations and show how pooling layers can significantly expand it. Finally, we'll delve into the differences between theoretical and effective receptive fields, providing insights on how CNNs utilize information during training.
If you want to dive deeper into the topic of the receptive field, here are some references that you might find useful:
- arxiv.org/abs/1701.04128
- theaisummer.com/receptive-field/
- distill.pub/2019/computing-re...
Chapters:
00:00 Intro
01:10 Receptive Field Basics
03:00 Receptive Field Calculation
05:14 Example Network Analysis
06:12 Pooling Layers
07:18 Effective Receptive Field
10:15 Outro
This video is animated using Manim, the Python animation library created by Grant Sanderson from @3blue1brown. Remember to like and subscribe to support the channel.
#artificialintelligence #animation #deeplearning #ai #convolutionalneuralnetworks #receptivefield #python #tutorial

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