Linganisha mbinu

	Ujifunzaji Unganishi Unaojisimamia (Ensemble Self-supervised Learning)	Ufumbuzi wa Maarifa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2020–2021	2015
Mwanzilishi	Multiple contributors (Grill et al., Caron et al., Chen et al.)	Hinton, G., Vinyals, O. & Dean, J.
Aina	Ensemble of self-supervised models or objectives	Neural network compression (teacher–student)
Chanzo asilia	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.
Majina mbadala	ensemble SSL, multi-view self-supervised ensemble, self-supervised ensemble learning, SSL ensemble	Bilgi Damıtma (Knowledge Distillation), bilgi damıtma, teacher-student distillation, model distillation
Zinazohusiana	5	5
Muhtasari	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.