Class activation mapping is a technique that uses global average pooling to visualize important regions in images that CNNs use to identify objects. It works by applying global average pooling to activation maps of the last convolutional layer to obtain the importance of each region for predicting the class. The technique was proposed to localize objects for weakly supervised tasks and help understand what CNNs learn from images.

1. Learning Deep Features for
Discriminative Localization
Mar 14, 2021
HeeDae Kwon

2. Contents
• Introduction
• Related work
• Class Activation Mapping
• Results

3. Introduction
GAP
Class activation mapping

4. FC-layer
Lost ability Classification

5. GAP
• Decrease parameters
• Prevent overfitting

6. Related Work
• Weakly – supervised
object localization
• Visualizing CNNs

7. Class Activation
Mapping

8. Class Activation Mapping

9. Class
Activation
Mapping

10. Class Activation Mapping

11. Results

12. 감사합니다