Сравнение методов

	Автоэнкодер	Transformer (NLP)
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2006	2017
Автор метода	Hinton, G.E. & Salakhutdinov, R.R.	Vaswani, A. et al.
Тип	Neural network (encoder-decoder)	Attention-based deep neural network
Основополагающий источник	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Другие названия	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Связанные	4	4
Сводка	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.