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LATENT SPACE AND TEXT-BASED GENERATIVE ADVERSARIAL NETWORKS (LATEXT-GANS) FOR TEXT GENERATION
LATENT SPACE AND TEXT-BASED GENERATIVE ADVERSARIAL NETWORKS (LATEXT-GANS) FOR TEXT GENERATION
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机译:用于文本生成的潜在空间和基于文本的生成逆向网络(LATEXT-GANS)
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摘要
According to embodiments, an encoder neural network receives a one-hot representation of a real text. The encoder neural network outputs a latent representation of the real text. A decoder neural network receives random noise data or artificial code generated by a generator neural network from random noise data. The decoder neural network outputs softmax representation of artificial text. The decoder neural network receives the latent representation of the real text. The decoder neural network outputs a reconstructed softmax representation of the real text. A hybrid discriminator neural network receives a first combination of the soft-text and the latent representation of the real text and a second combination of the softmax representation of artificial text and the artificial code. The hybrid discriminator neural network outputs a probability indicating whether the second combination is similar to the first combination. Additional embodiments for utilizing latent representation are also disclosed.
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