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| Sieć kapsułkowa× | Destylacja wiedzy× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2017 | 2015 |
| Twórca≠ | Sabour, S., Frosst, N. & Hinton, G. E. | Hinton, G., Vinyals, O. & Dean, J. |
| Typ≠ | Deep learning architecture (vector capsules with dynamic routing) | Neural network compression (teacher–student) |
| Źródło pierwotne≠ | Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS). link ↗ | Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop. link ↗ |
| Inne nazwy | Kapsül Ağı (CapsNet), CapsNet, capsule net, dynamic routing network | Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. | Knowledge Distillation is a model-compression technique, introduced by Geoffrey Hinton and colleagues in 2015, that trains a small student model using the soft-label outputs of a large teacher model. Distilled models such as DistilBERT and TinyBERT reach roughly 97% of the larger model's performance while running far faster. |
| ScholarGateZbiór danych ↗ |
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