Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Variational Autoencoder× | Autoencoder× | Difusioonimudel× | |
|---|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning | Machine learning |
| Tekkeaasta≠ | 2014 | 2006 | 2020 |
| Looja≠ | Kingma, D. P. & Welling, M. | Hinton, G.E. & Salakhutdinov, R.R. | Ho, J., Jain, A. & Abbeel, P. |
| Tüüp≠ | Deep generative latent-variable model (encoder–decoder) | Neural network (encoder-decoder) | Generative deep learning (denoising diffusion) |
| Algallikas≠ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ | Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507. DOI ↗ | Ho, J., Jain, A. & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. NeurIPS. link ↗ |
| Rööpnimetused≠ | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model | Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM |
| Seotud≠ | 5 | 4 | 4 |
| Kokkuvõte≠ | 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. | An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data. | 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. |
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