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Pooling Operations in Deep Learning: From "Invariable" to "Variable".

Zhou Tao1,2, Chang XiaoYu1, Lu HuiLing3

  • 1School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China.

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|June 30, 2022
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Summary
This summary is machine-generated.

Pooling operations in deep learning, crucial for image processing, reduce model complexity. This study standardizes pooling, categorizes operations, and analyzes their trade-offs for better model development.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deep learning, particularly in image processing, relies heavily on pooling operations.
  • Pooling reduces feature dimensions, parameters, and computational complexity, enhancing model efficiency.

Purpose of the Study:

  • To standardize the expression and analysis of pooling operations in deep learning.
  • To categorize pooling operations based on domain and kernel variability.
  • To evaluate the advantages and disadvantages of different pooling strategies.

Main Methods:

  • Summarizing pooling operation steps into domain, kernel, step size, activation, and response values.
  • Analyzing pooling domain and kernel from an invariable to variable perspective.
  • Classifying pooling operations into four distinct categories.

Main Results:

  • Standardized expression forms for pooling operations.
  • A novel classification of pooling operations: invariable domain, variable domain, variable kernel, and invariable+variable pooling.
  • Detailed discussion on the pros and cons of each pooling category.

Conclusions:

  • The research provides a systematic framework for understanding pooling operations.
  • Standardization and categorization are significant for the iterative updating and advancement of deep learning models.