Overview

  • Number of checkpoints: 30

  • Number of configs: 30

  • Number of papers: 15

    • ALGORITHM: 15

For supported datasets, see datasets overview.

Inpainting Models

  • Number of checkpoints: 8

  • Number of configs: 8

  • Number of papers: 4

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

    • [ALGORITHM] Generative Image Inpainting With Contextual Attention (⇨)

    • [ALGORITHM] Globally and Locally Consistent Image Completion (⇨)

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

Matting Models

  • Number of checkpoints: 9

  • Number of configs: 9

  • Number of papers: 3

    • [ALGORITHM] Deep Image Matting (⇨)

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

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

Super-Resolution Models

  • Number of checkpoints: 11

  • Number of configs: 11

  • Number of papers: 6

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

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

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

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

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

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

Generation Models

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 2

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

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