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Overview

  • Number of checkpoints: 71

  • Number of configs: 65

  • Number of papers: 25

    • ABSTRACT: 25

For supported datasets, see datasets overview.

Inpainting Models

  • Number of checkpoints: 8

  • Number of configs: 8

  • Number of papers: 4

    • [ABSTRACT] Free-Form Image Inpainting With Gated Convolution ()

    • [ABSTRACT] Generative Image Inpainting With Contextual Attention ()

    • [ABSTRACT] Globally and Locally Consistent Image Completion ()

    • [ABSTRACT] Image Inpainting for Irregular Holes Using Partial Convolutions ()

Matting Models

  • Number of checkpoints: 9

  • Number of configs: 9

  • Number of papers: 3

    • [ABSTRACT] Deep Image Matting ()

    • [ABSTRACT] Indices Matter: Learning to Index for Deep Image Matting ()

    • [ABSTRACT] Natural Image Matting via Guided Contextual Attention ()

Super-Resolution Models

  • Number of checkpoints: 44

  • Number of configs: 38

  • Number of papers: 16

    • [ABSTRACT] Basicvsr: The Search for Essential Components in Video Super-Resolution and Beyond ( )

    • [ABSTRACT] Basicvsr++: Improving Video Super-Resolution With Enhanced Propagation and Alignment ()

    • [ABSTRACT] Deep Face Super-Resolution With Iterative Collaboration Between Attentive Recovery and Landmark Estimation ()

    • [ABSTRACT] Edvr: Video Restoration With Enhanced Deformable Convolutional Networks ()

    • [ABSTRACT] Enhanced Deep Residual Networks for Single Image Super-Resolution ()

    • [ABSTRACT] Esrgan: Enhanced Super-Resolution Generative Adversarial Networks ()

    • [ABSTRACT] Glean: Generative Latent Bank for Large-Factor Image Super-Resolution ()

    • [ABSTRACT] Image Super-Resolution Using Deep Convolutional Networks ()

    • [ABSTRACT] Investigating Tradeoffs in Real-World Video Super-Resolution ()

    • [ABSTRACT] Learning Continuous Image Representation With Local Implicit Image Function ()

    • [ABSTRACT] Learning Texture Transformer Network for Image Super-Resolution ()

    • [ABSTRACT] Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network ()

    • [ABSTRACT] Real-Esrgan: Training Real-World Blind Super-Resolution With Pure Synthetic Data ()

    • [ABSTRACT] Residual Dense Network for Image Super-Resolution ()

    • [ABSTRACT] Tdan: Temporally-Deformable Alignment Network for Video Super-Resolution ()

    • [ABSTRACT] Video Enhancement With Task-Oriented Flow ()

Generation Models

  • Number of checkpoints: 10

  • Number of configs: 10

  • Number of papers: 2

    • [ABSTRACT] Image-to-Image Translation With Conditional Adversarial Networks ()

    • [ABSTRACT] Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks ()

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