Continuous activation map generation at training process

バージョン 1.0.3 (10 MB) 作成者: Kenta
Please click the thumbnail to watch the GIF file. 概要はサムネイルをクリックして下さい.This demo shows how to continuously create a class activation mapping.

ダウンロード 119 件

更新 2020/3/22

GitHub から

GitHub でライセンスを表示

This demo shows how to continuously create a class activation mapping (CAM) during the training process with a custom learning rate schedule.
Automatic differentiation enables you to customize CNN as you want. This example trains a network to classify data and simulteniously compute the CAM (Class Activation Mapping) of the validation data with the weights during the training.
This demo can visualize how the CNNs get to focus on the region in the image to classify which leads to the reability of the network and helps a lot in education of CNNs. Further, if the CNN is over-tuned to the dataset, the process also can be visualized.
The class activation mapping was done referring to the paper below.
Zhou, Bolei, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. "Learning deep features for discriminative localization." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929. 2016.
This demo using the custom training loop was made with the official document below.
If you like to explore the reason of the classification behind the network for the test image, you can use this demo (



Kenta (2022). Continuous activation map generation at training process (, GitHub. 取得済み .

MATLAB リリースの互換性
作成: R2020a
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!
この GitHub アドオンでの問題を表示または報告するには、GitHub リポジトリにアクセスしてください。
この GitHub アドオンでの問題を表示または報告するには、GitHub リポジトリにアクセスしてください。