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Found 7 papers, 1 papers with code
Iterative collaborative routing among equivariant capsules for transformation-robust capsule networks
Transformation-robustness is an important feature for machine learning models that perform image classification.
Robustcaps: a transformation-robust capsule network for image classification
This learning of such relationships allows our model to outperform both capsule and convolutional neural network baselines on transformation-robust classification tasks.
Can you even tell left from right? Presenting a new challenge for VQA
In order to study compositional generalisation, simple reasoning and memorisation, each scene of UOUC is annotated with up to 10 novel questions.
Learning Compositional Structures for Deep Learning: Why Routing-by-agreement is Necessary
We present a formal grammar description of convolutional neural networks and capsule networks that shows how capsule networks can enforce such parse-tree structures, while CNNs do not.
Building Deep Equivariant Capsule Networks
This is done using a trainable, equivariant function defined over a grid of group-transformations.
Building Deep, Equivariant Capsule Networks
This is done using a trainable, equivariant function defined over a grid of group-transformations.
PAC-learning is Undecidable
The problem of attempting to learn the mapping between data and labels is the crux of any machine learning task.
