Faster and more precisely than Grad-CAM
Faster and more precisely than Grad-CAM.
Upper result is...
Adapting Grad-CAM for Embedding Networks(arXiv, Jan 2020)
We changed below.
command below
python3 janken_demo.py
press [s] to change below mode(like ObjectDetection).
Detail is here(Japanese).
Look at Train_Faster-Grad-CAM.ipynb
1.Anomaly detection
When you use Self-supervised-learning, anomaly region is visualized by using Faster-Grad-CAM.
Next example is that circle is normal.
And extra line or missing line is anomaly image.
Upper result is that only normal images is used in trainging!
Realtime visualization is like below.
You can do anomaly detection and visualization at the same time.
2.Auto-Annotation
Auto-Annotation is based Grad-CAM and Bayesian optimization.
When you use Faster-Grad-CAM instead of Grad-CAM, you reduce total time by 25%(from 20sec to 15sec).