# Non-Iterative Cluster Routing: Analysis and Implementation Strategies
## 2 Related Works  
### Agreement Routing in Capsule Network  
CapsNets play a crucial role in feature encoding. They transform the extracted features into capsules, which are vectors capable of characterizing parts of an object, unlike the singular units used in conventional CNNs. These capsules possess the ability to represent more intricate aspects of an object, such as its pose, orientation, and texture style. The capsule system proposed in the dynamic routing paper titled \"Dynamic Routing Between Capsules\" [1] employs a routing-by-agreement mechanism. This mechanism iteratively determines its predictions based on the agreement between lower- and higher-level capsules, which is achieved through adjusting the weights connecting them. Inspired by the ideas presented in this paper, numerous subsequent research papers have explored similar concepts with the aim of enhancing the capsule network while preserving the critical notion of the part-whole relationship. These endeavors have led to diverse approaches and innovations in the field. The matrix capsules with EM routing approach [4] uses high-dimensional coincidence filtering and a fast iterative process to determine the routing weights, resulting in better performance and higher robustness to adversarial attacks than baseline CNNs. RS-CapsNet [8] integrates _Res2 Net_ and _Squeeze-and-Excitation_ blocks to extract multi-scale features, emphasizes useful information, and employs linear combinations between capsules in order to enhance object representation while reducing the capsule count. Cluster routing [7] uses variance as the fundamental indicator of agreement and confidence in the information encoded within the vote cluster. Group normalization [9] also utilizes the mean and standard deviation of a group. It splits the output channels of a convolutional layer into several groups and normalizes the features within each group according to the mean and standard deviation. However, as it does not use routing weights based on the agreement between capsules (data-dependent routing) to produce the input for the next capsule layer, as in cluster routing, it is not classified as a routing algorithm. CapsNets can outperform conventional CNNs in various applications, utilizing fewer parameters [10; 11; 12]. This advantage is particularly notable in medical image processing, where the main obstacles include detecting small lesions and overcoming class imbalances. Unlike CNNs, which often require substantial amounts of labeled data (that may be scarce in medical contexts), CapsNets can achieve similar levels of performance with a smaller data set [13; 14]. CapsNets employ capsules that encapsulate richer feature vectors, unlike CNNs that use scalar neurons, making them more effective in addressing these challenges [13]. Furthermore, CapsNets are superior in maintaining part-whole relationships and geometric details, significantly improving their performance in medical segmentation tasks [11; 12; 15; 16]. AI-generated deepfakes, including face-swapping videos and images, have been proliferating across the internet, driven by significant advancements in graphics processing units and AI algorithms. These technologies enable individuals to effortlessly create manipulated and unethical media. In areas such as deepfake detection, where novel and unforeseeable attacks are frequent, the strong generalization capability of capsules becomes vital. The nature of deepfake technology allows attackers to continuously develop new methods to bypass detection systems, making it imperative for defense mechanisms to possess the ability to generalize from known attacks to novel ones effectively. CapsNets are particularly suited to this task, due to their ability to understand the underlying structure of the data in a way that mirrors human visual perception. This understanding includes recognizing when an image or video deviates from the norm in a manner that suggests manipulation, even if the specific technique used for manipulation has not been encountered by the system before.