Unsupervised learning of global factors in deep generative models
| Autor: | Ignacio Peis, Pablo M. Olmos, Antonio Artés-Rodríguez |
|---|---|
| Přispěvatelé: | Comunidad de Madrid, Ministerio de Ciencia e Innovación (España), European Commission |
| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Telecomunicaciones Global factors Disentanglement Deep generative models Machine Learning (stat.ML) Unsupervised learning Representation learning Machine Learning (cs.LG) Statistics - Machine Learning Artificial Intelligence VAE Signal Processing Computer Vision and Pattern Recognition Software |
| Zdroj: | Pattern Recognition. 134:109130 |
| ISSN: | 0031-3203 |
| DOI: | 10.1016/j.patcog.2022.109130 |
| Popis: | We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contrast to the recent semi-supervised alternatives for global modeling in deep generative models, our approach combines a mixture model in the local or data-dependent space and a global Gaussian latent variable, which lead us to obtain three particular insights. First, the induced latent global space captures interpretable disentangled representations with no user-defined regularization in the evidence lower bound (as in beta-VAE and its generalizations). Second, we show that the model performs domain alignment to find correlations and interpolate between different databases. Finally, we study the ability of the global space to discriminate between groups of observations with non-trivial underlying structures, such as face images with shared attributes or defined sequences of digits images. This work has been partly supported by Spanish government (AEI/MCI) under grants PID2021-123182OB-I00, PID2021-125159NB-I00 and RTI2018-099655-B-100, by Comunidad de Madrid under grant IND2022/TIC-23550, by the European Union (FEDER) and the European Research Council (ERC) through the European Union's Horizon 2020 research and innovation program under Grant 714161, and by Comunidad de Madrid and FEDER through IntCARE-CM. The work of Ignacio Peis has been also supported by by Spanish government (MIU) under grant FPU18/00516. |
| Databáze: | OpenAIRE |
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