Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Selgitatav muutuv autoenkooder× | Multimodaalne varieeruv autoenkooder× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2013–2017 | 2018 |
| Looja≠ | Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement) | Wu, M. and Goodman, N. |
| Tüüp≠ | Generative model with interpretable latent space | Generative latent-variable model |
| Algallikas≠ | Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014). link ↗ | Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ |
| Rööpnimetused | XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model | MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model |
| Seotud≠ | 4 | 3 |
| Kokkuvõte≠ | An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to input features. It retains the VAE's generative power while adding transparency required in scientific and high-stakes applications. | 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. |
| ScholarGateAndmestik ↗ |
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