So sánh phương pháp

	Bộ tự mã hóa	Mô hình khuếch tán	Phân tích thành phần chính
Lĩnh vực	Học sâu	Học sâu	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2006	2020	2002
Người khởi xướng	Hinton, G.E. & Salakhutdinov, R.R.	Ho, J., Jain, A. & Abbeel, P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Loại	Neural network (encoder-decoder)	Generative deep learning (denoising diffusion)	Unsupervised dimensionality reduction
Công trình gốc	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Tên gọi khác	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Liên quan	4	4	3
Tóm tắt	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 diffusion model is a generative deep-learning method, introduced by Ho, Jain and Abbeel in 2020 (DDPM), that learns to produce high-quality images, audio and molecular structures by reversing a step-by-step noising process. It has largely displaced GANs as the current state of the art in generative modelling.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.