To be writen

Convolutional Neural Networks (CNNs) and

other deep networks have enabled unprecedented breakthroughs in a

variety  of  computer  vision  tasks,  from  image  classification  to

object detection  and segmentation. As in an intuitive scene, all these

tasks can be done with an unified framework with the localization

information gradually been expressed when the layers goes deeper. While

tackling with such a problem, we are also explaining why a model predict

and what a model predict.

In previous work, Zhou proposed a technique called Class Activation

Mapping (CAM) for identifying discriminative regions used by a

restricted class of image classification CNNs which replace full

connected layers with global average pooling layers to keep the

localization information. Ramprasaath used a gradient based method call

Gradient weighted Class Activation Mapping for optimizing CAM which

changes the base model with retaining. Above methods shows a good

visualization of most discriminative regions and be applied to

localization tasks.