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
Loss
Pros
- 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
Cons
- 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.