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?

- Vector Database 101: Everything You Need to Know
- Retrieval Augmented Generation (RAG) 101
- How to Pick the Right Vector Database for Your Use Case
- Exploring Vector Database Use Cases
- Mastering Audio AI
- 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 impact of model size on RL performance?
The size of a model, in the context of reinforcement learning (RL), significantly impacts its performance in various way
What is a distributed SQL database?
A distributed SQL database is a type of database that spreads its data across multiple locations or servers while still
How do you use the BETWEEN operator in SQL?
The BETWEEN operator in SQL is used to filter records within a certain range. It allows you to specify a lower and upper