Bandingkan kaedah

	Model Penyerakan Terpendam	SimCLR	Transformer Visi
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2022	2020	2021
Pengasas	Robin Rombach	Ting Chen	Dosovitskiy, A. et al.
Jenis	Neural network architecture	Neural network architecture	Transformer architecture for images (self-attention over patches)
Sumber perintis	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).	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597-1607). PMLR.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
Alias	LDM, Stable Diffusion, Latent Diffusion	Simple contrastive learning, SimCLR framework	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
Berkaitan	4	4	5
Ringkasan	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.	SimCLR is a self-supervised learning framework introduced by Chen et al. in 2020 that learns visual representations by contrasting similar and dissimilar views of images. The method applies strong data augmentations to create different views of the same image, then trains an encoder to bring similar views close in representation space while pushing dissimilar views apart.	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).