手法を比較

	T5（Text-to-Text Transfer Transformer）	転移学習
分野	深層学習	機械学習
系統	Machine learning	Machine learning
提唱年	2020	2010 (formalized); 1990s (early roots)
提唱者	Raffel, C.; Shazeer, N.; Roberts, A.; et al. (Google Brain)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
種類	Pre-trained encoder-decoder Transformer (sequence-to-sequence)	Learning paradigm
原典	Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research, 21(140), 1–67.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
別名	T5, Text-to-Text Transfer Transformer, T5-Small, T5-Base	TL, domain adaptation, fine-tuning, pre-trained model adaptation
関連	2	3
概要	T5 is a unified sequence-to-sequence deep learning framework introduced by Raffel et al. at Google Brain in 2020, published in the Journal of Machine Learning Research (Vol. 21, No. 140). It reframes every NLP task — classification, translation, summarisation, question answering, and more — as a text-to-text problem: both input and output are always character strings, enabling a single encoder-decoder Transformer to be pre-trained once and fine-tuned across tasks with a consistent interface. T5 introduced span-corruption pre-training and the C4 corpus, and its largest variant (11B parameters) achieved state-of-the-art results across a wide range of NLP benchmarks at the time of publication.	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.