Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Autoencoder Variacional× | Diffusion Model× | Rede Adversarial Generativa× | |
|---|---|---|---|
| Área | Aprendizado profundo | Aprendizado profundo | Aprendizado profundo |
| Família | Machine learning | Machine learning | Machine learning |
| Ano de origem≠ | 2014 | 2020 | 2014 |
| Autor original≠ | Kingma, D. P. & Welling, M. | Ho, J., Jain, A. & Abbeel, P. | Goodfellow, I. et al. |
| Tipo≠ | Deep generative latent-variable model (encoder–decoder) | Generative deep learning (denoising diffusion) | Generative deep learning (adversarial two-network game) |
| Fonte seminal≠ | 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 ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Outros nomes≠ | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model | Difüzyon Modeli (DDPM / Stable Diffusion), difüzyon modeli, denoising diffusion model, DDPM | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Relacionados≠ | 5 | 4 | 4 |
| Resumo≠ | 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. | 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. |
| ScholarGateConjunto de dados ↗ |
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