方法证据记录
Multimodal Variational Autoencoder
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Multimodal Variational Autoencoder (MVAE)
分类方法记录 · ml-model / deep-learning
- Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31. · URL
- Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). · URL
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