Comparar métodos

	Aprendizaje por transferencia con modelos de difusión	Modelo de difusión autosupervisado
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2020–2023	2020–2022
Autor original	Ho et al. (DDPM); transfer application popularized by Rombach et al. (Stable Diffusion) and Ruiz et al. (DreamBooth), 2020–2023	Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works
Tipo	Generative model with transfer learning	Generative model with self-supervised representation objective
Fuente seminal	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.
Alias	diffusion model fine-tuning, pre-trained diffusion transfer, TL-DM, domain-adapted diffusion model	SSDM, self-supervised score-based model, diffusion-based self-supervised learning, denoising diffusion with self-supervised pretraining
Relacionados	5	2
Resumen	Transfer Learning with Diffusion Models adapts a large pre-trained diffusion model — such as Stable Diffusion or DALL-E 2 — to a new target domain or task by continuing training on a smaller domain-specific dataset. Rather than learning the full generative process from scratch, practitioners leverage knowledge already encoded in millions of training steps to achieve high-quality domain-adapted generation with modest data and compute.	A self-supervised diffusion model couples the iterative noise-and-denoise generative process of denoising diffusion probabilistic models with a self-supervised representation learning objective — such as contrastive or masked prediction loss — so that the model simultaneously learns to generate realistic data and to produce semantically meaningful representations without any labeled examples.