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| Autoenkoder wariacyjny× | Model dyfuzyjny× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2014 | 2020 |
| Twórca≠ | Kingma, D. P. & Welling, M. | Ho, J., Jain, A. & Abbeel, P. |
| Typ≠ | Deep generative latent-variable model (encoder–decoder) | Generative deep learning (denoising diffusion) |
| Źródło pierwotne≠ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Inne nazwy≠ | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | 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. | 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. |
| ScholarGateZbiór danych ↗ |
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