{
  "id": "2206.09210",
  "version": "v1",
  "published": "2022-06-18T14:06:14.000Z",
  "updated": "2022-06-18T14:06:14.000Z",
  "title": "Multi-Modality Image Inpainting using Generative Adversarial Networks",
  "authors": [
    "Aref Abedjooy",
    "Mehran Ebrahimi"
  ],
  "categories": [
    "eess.IV",
    "cs.CV",
    "cs.LG"
  ],
  "abstract": "Deep learning techniques, especially Generative Adversarial Networks (GANs) have significantly improved image inpainting and image-to-image translation tasks over the past few years. To the best of our knowledge, the problem of combining the image inpainting task with the multi-modality image-to-image translation remains intact. In this paper, we propose a model to address this problem. The model will be evaluated on combined night-to-day image translation and inpainting, along with promising qualitative and quantitative results.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-06-18T14:06:14.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "generative adversarial networks",
      "multi-modality image inpainting",
      "multi-modality image-to-image translation remains intact",
      "night-to-day image translation",
      "image-to-image translation tasks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}