In traditional generative modeling, good data representation is very often a base for a good machine learning model. It can be linked to good representations encoding more explanatory factors that are hidden in the original data. With the invention of Generative Adversarial Networks (GANs), a subclass of generative models that are able to learn representations in an unsupervised and semi-supervised fashion, we are now able to adversarially learn good mappings from a simple prior distribution to a target data distribution. This paper presents an overview of recent developments in GANs with a focus on learning latent space representations.
CITATION STYLE
Zamorski, M., Zdobylak, A., Zięba, M., & Świątek, J. (2019). Generative Adversarial Networks: Recent Developments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11508 LNAI, pp. 248–258). Springer Verlag. https://doi.org/10.1007/978-3-030-20912-4_24
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