Cookie Consent
Hi, this website uses essential cookies to ensure its proper operation and tracking cookies to understand how you interact with it. The latter will be set only after consent.
Read our Privacy Policy
VOCABULARY

Pooling Layers in CNN

Pooling layers in convolutional neural networks (CNNs) are used to reduce the spatial dimensions (width and height) of the input volume for the next convolutional layer. They work by summarizing the features present in regions of the input.

Function of Pooling Layers

The most common form of pooling is max pooling, where the maximum element from the region of the input is selected. Pooling helps to reduce computation, control overfitting by providing an abstracted form of the representation, and makes the detection of features invariant to scale and orientation changes.

Lakera LLM Security Playbook
Learn how to protect against the most common LLM vulnerabilities

Download this guide to delve into the most common LLM security risks and ways to mitigate them.

Related terms
Activate
untouchable mode.
Get started for free.

Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.

Join our Slack Community.

Several people are typing about AI/ML security. 
Come join us and 1000+ others in a chat that’s thoroughly SFW.