Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Variational Autoencoder Adaptif Domain× | Jaringan Adversarial Generatif× | |
|---|---|---|
| Bidang | Pembelajaran Mendalam | Pembelajaran Mendalam |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2020 | 2014 |
| Pencetus≠ | Ilse, M.; Tomczak, J. M.; Louizos, C.; Welling, M. | Goodfellow, I. et al. |
| Tipe≠ | Generative model with domain adaptation | Generative deep learning (adversarial two-network game) |
| Sumber perintis≠ | 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 ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Alias | DA-VAE, domain-adaptive VAE, domain-conditioned variational autoencoder, cross-domain VAE | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Terkait≠ | 3 | 4 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
|
|