Capsule Network

replace traditional convolutional and pooling layers with a more biologically inspired architecture that better captures the spatial relationships between objects in an image
idea that the human visual system is composed of a hierarchy of “capsules” that process visual information at different levels of abstraction.
Each capsule comprises a group of neurons sensitive to specific features of an image, such as the presence of an edge or a particular shape.
These features are then combined and passed up the hierarchy to higher-level capsules, which extract more abstract concepts such as the identity of an object or the presence of a face.
Capsule Networks overcome the problem of Translational Invariance caused by CNNs.
Capsule Networks are able to capture better spatial relationship.
Capsule Networks uses better Downsampling methods which do not cause loss of information seen in CNNs.
Capsule Network perform much better than CNNs but are more computationally epensive.

Drawbacks of pooling layers

pooling layers, which down-sample the input image and can lead to the loss of important information about the spatial relationships between objects in the image.
Capsule networks aim to overcome this limitation by using a different down-sampling mechanism that preserves more spatial information.
Capsule Layer
Primary Capsule
Higher Layer Capsule

Capsule networks are more robust to image distortions and translations than traditional CNNs
They can maintain the spatial relationships between objects in an image
They can handle partially obscured objects better
They can be used for a variety of tasks, including object recognition and segmentation

Capsule networks are more complex and computationally expensive than traditional CNNs
They are a relatively new architecture, and there is still ongoing research to improve their performance and computational efficiency.