Pooling is a technique used in convolutional neural networks (CNNs) to reduce the spatial dimensions of feature maps while retaining important information. This makes the network more computationally efficient and helps prevent overfitting. The most common types are max pooling and average pooling. Max pooling selects the maximum value from each region of the feature map, preserving the most significant features while discarding less important details. For example, a 2x2 pooling layer reduces a 4x4 feature map to 2x2, simplifying computations in later layers. Pooling also adds translational invariance, meaning the network becomes less sensitive to small changes in the input's position. This is critical for tasks like image recognition, where objects may appear in different locations within an image. Pooling layers play a crucial role in the overall efficiency and robustness of CNNs.
What is “pooling” in a convolutional neural network?

- Getting Started with Milvus
- AI & Machine Learning
- Retrieval Augmented Generation (RAG) 101
- Natural Language Processing (NLP) Basics
- Accelerated Vector Search
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What is the difference between managed and unmanaged CaaS?
CaaS, or Container as a Service, offers developers a way to deploy and manage containerized applications. The difference
What is GPT-4’s performance compared to GPT-3?
GPT-4 shows notable improvements in performance compared to its predecessor, GPT-3, across various dimensions. One of th
What are the key principles of data governance?
Data governance refers to the overall management of data availability, usability, integrity, and security within an orga