পদ্ধতির তুলনা করুন

	ট্রান্সফার লার্নিং	ভিশন ট্রান্সফরমার
ক্ষেত্র	যন্ত্র শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2010 (formalized); 1990s (early roots)	2021
প্রবর্তক	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)	Dosovitskiy, A. et al.
ধরন	Learning paradigm	Transformer architecture for images (self-attention over patches)
মৌলিক উৎস	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
অপর নাম	TL, domain adaptation, fine-tuning, pre-trained model adaptation	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
সম্পর্কিত	3	5
সারসংক্ষেপ	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.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).