Módszerek összehasonlítása

	Ensemble öntanuló rendszerek (Ensemble Self-supervised Learning)	A tudásdesztilláció
Tudományterület	Gépi tanulás	Mélytanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	2020–2021	2015
Megalkotó	Multiple contributors (Grill et al., Caron et al., Chen et al.)	Hinton, G., Vinyals, O. & Dean, J.
Típus	Ensemble of self-supervised models or objectives	Neural network compression (teacher–student)
Alapmű	Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Ávila Pires, B., Guo, Z., Gheshlaghi Azar, M., Piot, B., Kavukcuoglu, K., Munos, R., & Valko, M. (2020). Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. Advances in Neural Information Processing Systems, 33, 21271–21284.	Hinton, G., Vinyals, O. & Dean, J. (2015). Distilling the Knowledge in a Neural Network. NeurIPS Deep Learning Workshop.
Alternatív nevek	ensemble SSL, multi-view self-supervised ensemble, self-supervised ensemble learning, SSL ensemble	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation
Kapcsolódó	5	5
Összefoglaló	Ensemble Self-supervised Learning combines multiple self-supervised models, objectives, or augmentation views into a unified framework to produce more robust and generalizable representations from unlabeled data. By aggregating diverse self-supervised signals, the ensemble reduces the risk of representation collapse and outperforms single-objective SSL approaches on downstream tasks.	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.