Abstract
A factored conditional restricted Boltzmann machine (FCRBM) is an efficient, compact model for multi-class temporal data (e.g. multi-label human motion data). However, since all factors in FCRBM are linked to the labels directly, data generated by the model is heavily dependent on the learned tags. In this paper, we propose a tree-based FCRBM model in which the factors are tree-like connected and only part of the factors are directly connected to the labels. The proposed model can make the newly generated data have a variety of sports styles and achieve a smooth transition between the styles using little or even no labeled data.
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Acknowledgments
This work was supported by the National Science Foundation of China (Grant Nos. 61375065 and 61625204), partially supported by the State Key Program of National Science Foundation of China (Grant Nos. 61432012 and 61432014).
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Xie, C., Lv, J., Jia, B., Xia, L. (2017). Tree Factored Conditional Restricted Boltzmann Machines for Mixed Motion Style. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_49
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DOI: https://doi.org/10.1007/978-3-319-70139-4_49
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