Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Självövervakat diffusionsmodell× | Variational Autoencoder× | |
|---|---|---|
| Ämnesområde | Djupinlärning | Djupinlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2020–2022 | 2014 |
| Upphovsperson≠ | Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works | Kingma, D. P. & Welling, M. |
| Typ≠ | Generative model with self-supervised representation objective | Deep generative latent-variable model (encoder–decoder) |
| Ursprungskälla≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Alias | SSDM, self-supervised score-based model, diffusion-based self-supervised learning, denoising diffusion with self-supervised pretraining | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Närliggande≠ | 2 | 5 |
| Sammanfattning≠ | 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
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