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Capsule Network/Evidence
Method evidence record

Capsule Network

A Capsule Network (CapsNet) is a deep learning architecture introduced by Sara Sabour, Nicholas Frosst and Geoffrey Hinton in 2017 that organises neurons as vectors (capsules) rather than scalar activations, so that spatial hierarchy and pose (orientation) information are encoded directly. It was proposed to overcome the fragility of convolutional networks to changes in viewpoint.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Capsule Network (CapsNet)
Taxonomic method record · ml-model / deep-learning
  • Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS). · URL
  • Hinton, G. E., Sabour, S. & Frosst, N. (2018). Matrix Capsules with EM Routing. International Conference on Learning Representations (ICLR). · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyKnowledge Distillationmachine-suggested · Relational suggestion, not evidence.Same method familyNeural Architecture Searchmachine-suggested · Relational suggestion, not evidence.Same method familyRandom Forestmachine-suggested · Relational suggestion, not evidence.Same method familySupport Vector Machinemachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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