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Learning Representations of Natural Language Texts with Generative Adversarial Networks at Document, Sentence, and Aspect Level
2018
Algorithms
The ability to learn robust, resizable feature representations from unlabeled data has potential applications in a wide variety of machine learning tasks. One way to create such representations is to train deep generative models that can learn to capture the complex distribution of real-world data. Generative adversarial network (GAN) approaches have shown impressive results in producing generative models of images, but relatively little work has been done on evaluating the performance of these
doi:10.3390/a11100164
fatcat:42tmwzgyorc4je42ym2xvbfpcy