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	Modele dyfuzyjne w przestrzeni utajonej	Mamba Wizyjny	Vision Transformer
Dziedzina	Uczenie głębokie	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2022	2024	2021
Twórca	Robin Rombach	Li Zhu	Dosovitskiy, A. et al.
Typ	Neural network architecture	Neural network architecture	Transformer architecture for images (self-attention over patches)
Źródło pierwotne	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).	Zhu, L., Liao, B., Zhang, Q., Wang, X., Liu, W., & Wang, X. (2024). Vision Mamba: Efficient state space models for image understanding. In International Conference on Machine Learning.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Inne nazwy	LDM, Stable Diffusion, Latent Diffusion	ViM, Mamba for Vision	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Pokrewne	4	4	5
Podsumowanie	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.	Vision Mamba is an efficient state space model approach for image understanding introduced in 2024 that adapts Mamba, a linear-complexity sequence model, to computer vision. By reformulating image tokens as sequences and using state space models, Vision Mamba achieves competitive accuracy with transformers while maintaining linear computational complexity.	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).