Сравнение на методи

	Автоенкодер	Конволюционна невронна мрежа (Класификация)	Трансформър (обработка на естествен език)
Област	Дълбоко обучение	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning	Machine learning
Година на възникване	2006	1998	2017
Създател	Hinton, G.E. & Salakhutdinov, R.R.	LeCun, Y. et al.	Vaswani, A. et al.
Тип	Neural network (encoder-decoder)	Deep neural network (convolutional)	Attention-based deep neural network
Основополагащ източник	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Други названия	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Свързани	4	5	4
Резюме	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.	A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.	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.