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 (⇨)