Name
Abstract | ||
|---|---|---|
•Learning global dependencies among observations in VAEs with a mixture prior and a global latent space.•Interpretability of the generative factors without introducing extra manipulation of the ELBO.•Mixing datasets leads to inferring local/global factors and perform domain alignment. |
Year | DOI | Venue |
|---|---|---|
2023 | 10.1016/j.patcog.2022.109130 | Pattern Recognition |
Keywords | DocType | Volume |
VAE,Deep generative models,Global factors,Unsupervised learning,Disentanglement,Representation learning | Journal | 134 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ignacio Peis | 1 | 0 | 0.34 |
| Pablo M. Olmos | 2 | 114 | 18.97 |
| Antonio Artés-Rodríguez | 3 | 206 | 34.76 |