Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Доменно-адаптивный вариационный автокодировщик× | Вариационный автокодировщик× | |
|---|---|---|
| Область | Глубокое обучение | Глубокое обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2020 | 2014 |
| Автор метода≠ | Ilse, M.; Tomczak, J. M.; Louizos, C.; Welling, M. | Kingma, D. P. & Welling, M. |
| Тип≠ | Generative model with domain adaptation | Deep generative latent-variable model (encoder–decoder) |
| Основополагающий источник≠ | Ilse, M., Tomczak, J. M., Louizos, C., & Welling, M. (2020). DIVA: Domain Invariant Variational Autoencoders. Proceedings of the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), PMLR 121, 322–348. link ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Другие названия | DA-VAE, domain-adaptive VAE, domain-conditioned variational autoencoder, cross-domain VAE | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Связанные≠ | 3 | 5 |
| Сводка≠ | A Domain-Adaptive Variational Autoencoder (DA-VAE) extends the standard VAE framework to learn disentangled latent representations that separate domain-specific variation from class-relevant and domain-invariant content, enabling models trained on a source domain to generalise effectively to a different but related target domain with limited or no target labels. | 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. |
| ScholarGateНабор данных ↗ |
|
|