পদ্ধতির তুলনা করুন

	বেয়েশিয়ান ফিউ-শট লার্নিং	ট্রান্সফার লার্নিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2018-2019	2010 (formalized); 1990s (early roots)
প্রবর্তক	Gordon et al.; Finn, Xu & Levine	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ধরন	Probabilistic meta-learning	Learning paradigm
মৌলিক উৎস	Gordon, J., Bronskill, J., Bauer, M., Nowozin, S. & Turner, R. E. (2019). Meta-Learning Probabilistic Inference for Prediction. International Conference on Learning Representations (ICLR 2019).	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
অপর নাম	Bayesian meta-learning, probabilistic few-shot learning, amortized Bayesian few-shot learning, Bayesian FSL	TL, domain adaptation, fine-tuning, pre-trained model adaptation
সম্পর্কিত	5	3
সারসংক্ষেপ	Bayesian few-shot learning combines Bayesian inference with meta-learning to enable a model to generalize from as few as one to five labeled examples per class. By treating task-specific parameters as random variables and learning an informative prior across many training tasks, the method produces calibrated uncertainty estimates alongside predictions — a key advantage over deterministic few-shot learners.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.