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        <h1>Tensor-to-Image: Image-to-Image Translation with Vision Transformers</h1>
        <h3><a href="yigitgunduc.github.io">Yigit Gunduc</a></h3>
        <h3><a href="">[GitHub]</a> <a href="">[Paper]</a></h3>
        <img width=640 height=480 src="assets/prev.jpeg">
        <h2>Abstract</h2>
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          <p>Transformers gain huge attention since they are first introduced and have a wide
          range of applications. Transformers start to take over all areas of deep
          learning and the Vision transformers paper also proved that they
          can be used for computer vision tasks. In this paper, we utilized a 
          vision transformer-based custom-designed model, tensor-to-image, for the image 
          to image translation. With the help of self-attention, our model
          was able to generalize and apply to different problems without a single 
          modification</p>
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        <h2>Code & Paper</h2>
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          <span>
            <a href="https://arxiv.org/abs/2110.08037"><img width="214" height="297" src="assets/paper.png"></a>
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            <a href="https://arxiv.org/abs/2110.08037"><p>Tensor-to-Image: Image-to-Image Translation with Vision Transformers</p></a>
            <p>For the full and please see the <a href="github.com/yigitgunduc/tensor-to-image">GitHub</a> repo</p>
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        <h2>Cite</h2>
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          <p>If you use this code for your research, please cite our paper <a href="https://arxiv.org/abs/2110.08037">Tensor-to-Image: Image-to-Image Translation with Vision Transformers</a></p>
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            <pre>
              <code>
  @article{gunducc2021tensor,
    title={Tensor-to-Image: Image-to-Image Translation with Vision Transformers}, 
    author={G{\"u}nd{\"u}{\c{c}}, Yi{\u{g}}it},
    journal={arXiv preprint arXiv:2110.08037},
    year={2021}
  }</code>
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