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Overview

  • Number of checkpoints: 178

  • Number of configs: 174

  • Number of papers: 46

    • ALGORITHM: 47

  • Tasks:

    • image2image translation

    • video super-resolution

    • video interpolation

    • text2image

    • colorization

    • matting

    • inpainting

    • image super-resolution

    • image generation

    • image2image

    • internal learning

    • unconditional gans

    • conditional gans

    • 3d-aware generation

    • image restoration

For supported datasets, see datasets overview.

AOT-GAN (TVCG’2021)

  • Tasks: inpainting

  • Number of checkpoints: 1

  • Number of configs: 1

  • Number of papers: 1

    • [ALGORITHM] Aggregated Contextual Transformations for High-Resolution Image Inpainting ()

BasicVSR (CVPR’2021)

  • Tasks: video super-resolution

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

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

BasicVSR++ (CVPR’2022)

  • Tasks: video super-resolution

  • Number of checkpoints: 7

  • Number of configs: 7

  • Number of papers: 1

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

BigGAN (ICLR’2019)

  • Tasks: conditional gans

  • Number of checkpoints: 7

  • Number of configs: 6

  • Number of papers: 1

    • [ALGORITHM] Large Scale {Gan ()

CAIN (AAAI’2020)

  • Tasks: video interpolation

  • Number of checkpoints: 1

  • Number of configs: 1

  • Number of papers: 1

    • [ALGORITHM] Channel Attention Is All You Need for Video Frame Interpolation ()

CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks (ICCV’2017)

  • Tasks: image2image translation

  • Number of checkpoints: 6

  • Number of configs: 6

  • Number of papers: 1

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

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (ICLR’2016)

  • Tasks: unconditional gans

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

    • [ALGORITHM] Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks ()

DeepFillv1 (CVPR’2018)

  • Tasks: inpainting

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

DeepFillv2 (CVPR’2019)

  • Tasks: inpainting

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

DIC (CVPR’2020)

  • Tasks: image super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

DIM (CVPR’2017)

  • Tasks: matting

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

    • [ALGORITHM] Deep Image Matting ()

Disco Diffusion

  • Tasks: text2image,image2image

  • Number of checkpoints: 2

  • Number of configs: 0

  • Number of papers: 1

    • [ALGORITHM] Disco-Diffusion ()

EDSR (CVPR’2017)

  • Tasks: image super-resolution

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

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

EDVR (CVPRW’2019)

  • Tasks: video super-resolution

  • Number of checkpoints: 4

  • Number of configs: 4

  • Number of papers: 1

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

EG3D (CVPR’2022)

  • Tasks: 3d-aware generation

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

    • [ALGORITHM] Efficient Geometry-Aware 3d Generative Adversarial Networks ()

ESRGAN (ECCVW’2018)

  • Tasks: image super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

FLAVR (arXiv’2020)

  • Tasks: video interpolation

  • Number of checkpoints: 1

  • Number of configs: 1

  • Number of papers: 1

    • [ALGORITHM] Flavr: Flow-Agnostic Video Representations for Fast Frame Interpolation ()

GCA (AAAI’2020)

  • Tasks: matting

  • Number of checkpoints: 4

  • Number of configs: 4

  • Number of papers: 1

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

GGAN (ArXiv’2017)

  • Tasks: unconditional gans

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

    • [ALGORITHM] Geometric Gan ()

GLEAN (CVPR’2021)

  • Tasks: image super-resolution

  • Number of checkpoints: 4

  • Number of configs: 7

  • Number of papers: 1

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

Global&Local (ToG’2017)

  • Tasks: inpainting

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

Guided Diffusion (NeurIPS’2021)

  • Tasks: image generation

  • Number of checkpoints: 2

  • Number of configs: 0

  • Number of papers: 1

    • [ALGORITHM] Diffusion Models Beat Gans on Image Synthesis ()

IconVSR (CVPR’2021)

  • Tasks: video super-resolution

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

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

IndexNet (ICCV’2019)

  • Tasks: matting

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

Instance-aware Image Colorization (CVPR’2020)

  • Tasks: colorization

  • Number of checkpoints: 1

  • Number of configs: 1

  • Number of papers: 1

    • [ALGORITHM] Instance-Aware Image Colorization ()

LIIF (CVPR’2021)

  • Tasks: image super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

LSGAN (ICCV’2017)

  • Tasks: unconditional gans

  • Number of checkpoints: 4

  • Number of configs: 4

  • Number of papers: 1

    • [ALGORITHM] Least Squares Generative Adversarial Networks ()

NAFNet (ECCV’2022)

  • Tasks: image restoration

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

    • [ALGORITHM] Simple Baselines for Image Restoration ()

PConv (ECCV’2018)

  • Tasks: inpainting

  • Number of checkpoints: 2

  • Number of configs: 4

  • Number of papers: 1

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

PGGAN (ICLR’2018)

  • Tasks: unconditional gans

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

    • [ALGORITHM] Progressive Growing of Gans for Improved Quality, Stability, and Variation ()

Pix2Pix (CVPR’2017)

  • Tasks: image2image translation

  • Number of checkpoints: 4

  • Number of configs: 4

  • Number of papers: 1

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

Positional Encoding in GANs

  • Tasks: unconditional gans

  • Number of checkpoints: 21

  • Number of configs: 21

  • Number of papers: 1

    • [ALGORITHM] Positional Encoding as Spatial Inductive Bias in Gans ()

RDN (CVPR’2018)

  • Tasks: image super-resolution

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

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

RealBasicVSR (CVPR’2022)

  • Tasks: video super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

    • [ALGORITHM] Realbasicvsr: Investigating Tradeoffs in Real-World Video Super-Resolution ()

Real-ESRGAN (ICCVW’2021)

  • Tasks: image super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

SAGAN (ICML’2019)

  • Tasks: conditional gans

  • Number of checkpoints: 9

  • Number of configs: 6

  • Number of papers: 1

    • [ALGORITHM] Self-Attention Generative Adversarial Networks ()

SinGAN (ICCV’2019)

  • Tasks: internal learning

  • Number of checkpoints: 3

  • Number of configs: 3

  • Number of papers: 1

    • [ALGORITHM] Singan: Learning a Generative Model From a Single Natural Image ()

SNGAN (ICLR’2018)

  • Tasks: conditional gans

  • Number of checkpoints: 10

  • Number of configs: 6

  • Number of papers: 1

    • [ALGORITHM] Spectral Normalization for Generative Adversarial Networks ()

SRCNN (TPAMI’2015)

  • Tasks: image super-resolution

  • Number of checkpoints: 1

  • Number of configs: 1

  • Number of papers: 1

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

SRGAN (CVPR’2016)

  • Tasks: image super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

StyleGANv1 (CVPR’2019)

  • Tasks: unconditional gans

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

    • [ALGORITHM] A Style-Based Generator Architecture for Generative Adversarial Networks ()

StyleGANv2 (CVPR’2020)

  • Tasks: unconditional gans

  • Number of checkpoints: 12

  • Number of configs: 12

  • Number of papers: 1

    • [ALGORITHM] Analyzing and Improving the Image Quality of Stylegan ()

StyleGANv3 (NeurIPS’2021)

  • Tasks: unconditional gans

  • Number of checkpoints: 9

  • Number of configs: 10

  • Number of papers: 1

    • [ALGORITHM] Alias-Free Generative Adversarial Networks ()

TDAN (CVPR’2020)

  • Tasks: video super-resolution

  • Number of checkpoints: 2

  • Number of configs: 4

  • Number of papers: 1

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

TOFlow (IJCV’2019)

  • Tasks: video super-resolution,video interpolation

  • Number of checkpoints: 6

  • Number of configs: 6

  • Number of papers: 1

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

TTSR (CVPR’2020)

  • Tasks: image super-resolution

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

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

WGAN-GP (NeurIPS’2017)

  • Tasks: unconditional gans

  • Number of checkpoints: 2

  • Number of configs: 2

  • Number of papers: 1

    • [ALGORITHM] Improved Training of Wasserstein Gans ()

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