Vertaile menetelmiä

	Vision Transformer	Diffuusiomalli	Generatiivinen kilpaileva verkko	Random Forest
Tieteenala	Syväoppiminen	Syväoppiminen	Syväoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning	Machine learning
Syntyvuosi	2021	2020	2014	2001
Kehittäjä	Dosovitskiy, A. et al.	Ho, J., Jain, A. & Abbeel, P.	Goodfellow, I. et al.	Breiman, L.
Tyyppi	Transformer architecture for images (self-attention over patches)	Generative deep learning (denoising diffusion)	Generative deep learning (adversarial two-network game)	Ensemble (bagging of decision trees)
Alkuperäislähde	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.	Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS.	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Rinnakkaisnimet	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images	Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Liittyvät	5	4	4	4
Tiivistelmä	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).	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.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.