Glossary Library

Technical GlossaryComputer Vision

CNN Feature Maps

Intermediate representations learned by convolutional layers that carry visual patterns at different abstraction levels.

CNN feature maps are among the most fundamental intermediate products of modern visual representation learning. Early layers capture low-level patterns such as edges and textures, while deeper layers move toward object parts and semantic structure. These maps are useful not only for understanding what the model has learned, but also for extracting features for downstream tasks.

You Might Also Like

Explore these concepts to continue your artificial intelligence journey.

Glossary Cover

yapay-zeka-temelleri

Representation Learning

An approach in which informative, discriminative, and task-relevant internal representations are learned automatically from raw data.

Glossary Cover

makine-ogrenmesi

Non-negative Matrix Factorization

A dimensionality reduction technique that produces part-based and interpretable representations in non-negative data.

Glossary Cover

makine-ogrenmesi

Autoencoder-Based Dimensionality Reduction

An approach that learns lower-dimensional representations of data through neural-network-based compression.