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	Zamaskowane autoenkodery	Modele dyfuzyjne w przestrzeni utajonej	Vision Transformer
Dziedzina	Uczenie głębokie	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2021	2022	2021
Twórca	Kaiming He	Robin Rombach	Dosovitskiy, A. et al.
Typ	Neural network architecture	Neural network architecture	Transformer architecture for images (self-attention over patches)
Źródło pierwotne	He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16000-16009).	Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695).	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Inne nazwy	MAE, Vision MAE	LDM, Stable Diffusion, Latent Diffusion	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Pokrewne	4	4	5
Podsumowanie	Masked Autoencoders (MAE) is a self-supervised learning approach introduced by He et al. in 2021 that masks random patches of an image and trains a model to reconstruct the missing content. Adapting the masked language modeling paradigm from NLP to vision, MAE learns rich visual representations by solving a challenging reconstruction task without requiring labels.	Latent Diffusion Models (LDMs) are a generative approach introduced by Rombach et al. in 2022 that performs the diffusion process in a compressed latent space rather than pixel space, enabling efficient high-resolution image synthesis. By compressing images into a low-dimensional latent representation using a variational autoencoder, diffusion becomes computationally tractable while maintaining visual quality.	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).