Variational Auto-Encoder for text generation

Hu, Haojin; Liao, Mengfan; Mao, Weiming; Liu, Wei; Zhang, Chao; Jing, Yanmei

doi:10.1109/itoec49072.2020.9141571

Cited by 5 publications

(2 citation statements)

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“…The network structure of the generator is shown in Figure (1), and the structure of the Transformer Encoder is shown in Figure (2). In Figure (1), s is the convolution stride, k is the convolution kernel dimension, and n is the number of output channels.…”

Section: Transformer-based Generative Model Transsrganmentioning

confidence: 99%

“…The earliest generative model is the variational autoencoder [1] based on variational inference and Bayesian theory. Variational autoencoder can generate not only pictures, but also text [2] and audio [3]. Although the variational autoencoder is simple and effective, it tends to generate noisy data irrelevant to the trainset owing to the assumption of a simple normal distribution as the original sample distribution.…”

Section: Introductionmentioning

confidence: 99%

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Image Creation Based on Transformer and Generative Adversarial Networks

Liu

2022

IEEE Access

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To address the problem of low authenticity of generated images in existing generative models, the transformer super-resolution generative adversarial network(TransSRGAN) model based on the generative adversarial network is proposed. The generator of the model uses the transformer encoder sub-module as the basic module. The features of the input vector are extracted. low-definition images are generated through the transformer encoder submodule, and the low-definition image is up-sampled by the convolutional neural network to complete the image generation. The discriminator of this model uses the convolutional neural network as the basic module. To discriminate the real samples from the generated fake samples, the discriminator extracts the image features by the convolutional neural network. The experimental results show that the TransSRGAN model brings the distribution of the generated samples closer to the training samples, effectively raises the quality of the generated samples, improves the authenticity of the generated samples, and enriches the diversity of the generated samples. During the training process, there was no mode collapse or instability.

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Section: Transformer-based Generative Model Transsrganmentioning

confidence: 99%