Salīdzināt metodes

	Pašuzraudzēts Gausa process	Daļēji uzraudzīts Gausa process
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2019–2021	2004
Autors	Fortuin, V. et al.; broader self-supervised GP literature	Lawrence, N. D. & Jordan, M. I.
Tips	Probabilistic model (self-supervised GP pretraining + kernel learning)	Probabilistic model (semi-supervised)
Pirmavots	Fortuin, V., Rätsch, G., & Mandt, S. (2020). GP-VAE: Deep probabilistic time series imputation using Gaussian process variational autoencoders. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108, 1651–1661.	Lawrence, N. D., & Jordan, M. I. (2004). Semi-supervised learning via Gaussian processes. In Advances in Neural Information Processing Systems (NIPS), 17, 753–760. MIT Press.
Citi nosaukumi	SSL-GP, self-supervised GP, self-supervised GPR, self-supervised Gaussian process regression	SS-GP, semi-supervised GP, Gaussian process with unlabeled data, GP manifold learning
Saistītās	6	5
Kopsavilkums	Self-supervised Gaussian Process (SSL-GP) combines the principled uncertainty quantification of Gaussian processes with self-supervised pretraining, learning expressive kernels or latent representations from unlabeled data before fitting a GP on a small labeled set. This makes the approach especially powerful in low-labeled-data regimes where a conventional GP would overfit or produce poorly calibrated uncertainty estimates.	Semi-supervised Gaussian Process extends the probabilistic GP framework to exploit unlabeled data alongside a small set of labeled observations. By placing a GP prior over functions and leveraging the geometric structure revealed by unlabeled inputs, it learns more accurate and better-calibrated predictors than a purely supervised GP when labels are scarce, making it well suited for scientific and medical problems where annotation is expensive.