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Title: Siamese Capsule Networks 

Paper content:
Capsule Networks have shown encouraging results on \textit{defacto} benchmark computer vision datasets such as MNIST, CIFAR and smallNORB.
Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable.
Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points.
In doing so we introduce \textit{Siamese Capsule Networks}, a new variant that can be used for pairwise learning tasks.
We find that the model improves over baselines in the few-shot learning setting, suggesting that capsule networks are efficient at learning discriminative representations when given few samples.
We find that \textit{Siamese Capsule Networks} perform well against strong baselines on both pairwise learning datasets when trained using a contrastive loss with $\ell_2$-normalized capsule encoded pose features, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.
Convolutional Neural networks (CNNs) have been a mainstay model for a wide variety of tasks in computer vision.
CNNs are effective at detecting local features in the receptive field, although the spatial relationship between features is lost when crude routing operations are performed to achieve translation invariance, as is the case with max and average pooling.
Essentially, pooling results in viewpoint invariance so that small perturbations in the input do not effect the output.
This leads to a significant loss of information about the internal properties of present entities (e.g location, orientation, shape and pose) in an image and relationships between them.
The issue is usually combated by having large amounts of annotated data from a wide variety of viewpoints, albeit redundant and less efficient in many cases.
As noted by hinton1985shape, from a psychology perspective of human shape perception, pooling does not account for the coordinate frames imposed on objects when performing mental rotation to identify handedness BID23 ; BID19 BID12 .
Hence, the scalar output activities from local kernel regions that summarize sets of local inputs are not sufficient for preserving reference frames that are used in human perception, since viewpoint information is discarded.
Spatial Transformer Networks (STN) BID13 have acknowledged the issue by using dynamic spatial transformations on feature mappings to enhance the geometric invariance of the model, although this approach addresses changes in viewpoint by learning to remove rotational and scale variance, as opposed to viewpoint variance being reflected in the model activations.
Instead of addressing translation invariance using pooling operations, BID8 have worked on achieving translation equivariance.The recently proposed Capsule Networks BID24 ; BID7 have shown encouraging results to address these challenges.
Thus far, Capsule Networks have only been tested on datasets that have (1) a relatively sufficient number of instances per class to learn from and (2) utilized on tasks in the standard classification setup.
This paper extends Capsule Networks to the pairwise learning setting to learn relationships between whole entity encodings, while also demonstrating their ability to learn from little data that can perform few-shot learning where instances from new classes arise during testing (i.e zero-shot prediction).
The Siamese Capsule Network is trained using a contrastive loss with 2 -normalized encoded features and demonstrated on face verification tasks.
BID8 first introduced the idea of using whole vectors to represent internal properties (referred to as instantiation parameters that include pose) of an entity with an associated activation probability where each capsule represents a single instance of an entity within in an image.
This differs from the single scalar outputs in conventional neural networks where pooling is used as a crude routing operation over filters.
Pooling performs sub-sampling so that neurons are invariant to viewpoint change, instead capsules look to preserve the information to achieve equivariance, akin to perceptual systems.
Hence, pooling is replaced with a dynamic routing scheme to send lowerlevel capsule (e.g nose, mouth, ears etc.) outputs as input to parent capsule (e.g face) that represent part-whole relationships to achieve translation equivariance and untangles the coordinate frame of an entity through linear transformations.
The idea has its roots in computer graphics where images are rendered given an internal hierarchical representation, for this reason the brain is hypothesized to solve an inverse graphics problem where given an image the cortex deconstructs it to its latent hierarchical properties.
The original paper by BID24 describes a dynamic routing scheme that represent these internal representations as vectors given a group of designated neurons called capsules, which consist of a pose vector u ∈ R d and activation α ∈ [0, 1].
The architecture consists of two convolutional layers that are used as the initial input representations for the first capsule layer that are then routed to a final class capsule layer.
The initial convolutional layers allow learned knowledge from local feature representations to be reused and replicated in other parts of the receptive field.
The capsule inputs are determined using a Iterative Dynamic Routing scheme.
A transformation W ij is made to output vector u i of capsule C L i .
The length of the vector u i represents the probability that this lower-level capsule detected a given object and the direction corresponds to the state of the object (e.g orientation, position or relationship to upper capsule).
The output vector u i is transformed into a prediction vectorû j|i , whereû j|i = W ij u i .
Then,û j|i is weighted by a coupling coefficient c ij to obtain s j = i c ijûj|i , where coupling coefficients for each capsule j c ij = 1 and c ij is got by log prior probabilities b ij from a sigmoid function, followed by the softmax, c ij = e bij / k e b ik .
Ifû L j|i has high scalar magnitude when multiplied by u L+1 j then the coupling coefficient c ij is increased and the remaining potential parent capsules coupling coefficients are decreased.
Routing By Agreement is then performed using coincidence filtering to find tight clusters of nearby predictions.
The entities output vector length is represented as the probability of an entity being present by using the nonlinear normalization shown in Equation 1 where vote v j is the output from total input s j , which is then used to compute the agreement a ij = v jûj|i that is added to the log prior b ij .
This paper has introduced the Siamese Capsule Network, a novel architecture that extends Capsule Networks to the pairwise learning setting with a feature 2 -normalized contrastive loss that maximizes inter-class variance and minimizes intra-class variance.
The results indicate Capsule Networks perform better at learning from only few examples and converge faster when a contrastive loss is used that takes face embeddings in the form of encoded capsule pose vectors.
We find Siamese Capsule Networks to perform particularly well on the AT&T dataset in the few-shot learning setting, which is tested on unseen classes (i.e subjects) during testing, while competitive against baselines for the larger Labeled Faces In The Wild dataset.

assistant: text: A pairwise learned capsule network that performs well on face verification tasks given limited labeled data