方法对比
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| 多模态生成对抗网络× | 多模态变分自编码器× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2014–2016 | 2018 |
| 提出者≠ | Reed et al. (text-to-image GAN); foundation by Goodfellow et al. | Wu, M. and Goodman, N. |
| 类型≠ | Generative adversarial model | Generative latent-variable model |
| 开创性文献≠ | Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative adversarial text to image synthesis. Proceedings of the 33rd International Conference on Machine Learning (ICML), PMLR 48, 1060–1069. link ↗ | Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ |
| 别名 | MM-GAN, multimodal generative adversarial network, cross-modal GAN, multi-modal GAN | MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model |
| 相关≠ | 4 | 3 |
| 摘要≠ | A Multimodal GAN is a generative adversarial network conditioned on — or jointly learning across — more than one data modality (e.g., text descriptions, images, audio, or structured data). By fusing information from multiple sources, the generator can synthesize realistic outputs that respect cross-modal constraints, enabling tasks such as text-to-image synthesis, image-to-audio generation, and joint modality imputation. | The Multimodal Variational Autoencoder (MVAE) is a deep generative model that learns a shared latent representation across two or more data modalities — such as images and captions — using a product-of-experts fusion of modality-specific encoders, enabling generation and inference even when only a subset of modalities is observed at test time. |
| ScholarGate数据集 ↗ |
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