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| GAN multilingüe× | Generative Adversarial Network× | |
|---|---|---|
| Camp | Aprenentatge profund | Aprenentatge profund |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2017–2019 | 2014 |
| Autor original≠ | Goodfellow et al. (GAN); multilingual extensions by various authors from 2017 onward | Goodfellow, I. et al. |
| Tipus≠ | Generative adversarial model with multilingual conditioning | Generative deep learning (adversarial two-network game) |
| Font seminal≠ | Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27. link ↗ | Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS. link ↗ |
| Àlies | Multilingual GAN, Cross-lingual GAN, Multilingual Generative Adversarial Network, ML-GAN | Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network |
| Relacionats≠ | 5 | 4 |
| Resum≠ | A Multilingual GAN pairs the generative adversarial framework with cross-lingual components — a shared encoder, language-conditioned generator, and a language discriminator — so that a single model can generate or align representations across multiple languages simultaneously. It is applied to cross-lingual text generation, machine translation, multilingual data augmentation, and language-invariant feature learning. | 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. |
| ScholarGateConjunt de dades ↗ |
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