Corpus ID: 3353110

Image Transformer

@article{Parmar2018ImageT,
  title={Image Transformer},
  author={Niki Parmar and Ashish Vaswani and Jakob Uszkoreit and Lukasz Kaiser and Noam M. Shazeer and Alexander Ku and Dustin Tran},
  journal={ArXiv},
  year={2018},
  volume={abs/1802.05751}
}
Image generation has been successfully cast as an autoregressive sequence generation or transformation problem. [...] Key Result In a human evaluation study, we show that our super-resolution models improve significantly over previously published autoregressive super-resolution models. Images they generate fool human observers three times more often than the previous state of the art.Expand
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302 Citations
Highly Influential Citations
21
Background Citations
124
Methods Citations
101
Results Citations
4

302 Citations

Citation Type

Citation Type

Attention Augmented Convolutional Networks
Combining Transformer Generators with Convolutional Discriminators
ViViT: A Video Vision Transformer
Self-Attention Generative Adversarial Networks
Locally Masked Convolution for Autoregressive Models
Face sketch-to-photo transformation with multi-scale self-attention GAN
High-Fidelity Pluralistic Image Completion with Transformers
SCRAM: Spatially Coherent Randomized Attention Maps
Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation
Dual Contrastive Loss and Attention for GANs
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References

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The student-t mixture as a natural image patch prior with application to image compression
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Attention is All you Need
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Generating Images from Captions with Attention
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
BEGAN: Boundary Equilibrium Generative Adversarial Networks
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