Papers with Code 아카이브
Image Generation
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- 평가표
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/johngear/eecs504
https://github.com/johnnyjana730/ACGAN_animate
https://github.com/johnnyjana730/keras_dcgan_car
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/johnnylu305/resnet-50-101-152
https://github.com/johnrobinsn/diffusion_experiments/blob/main/DiffEdit.ipynb
DiffEdit: Diffusion-based semantic image editing with mask guidance
https://github.com/jokfun/FakePaint
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jonahwinninghoff/Text-Summarization
https://github.com/jonasz/progressive_infogan
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/jonbarron/robust_loss_pytorch
https://github.com/jongcye/CollaGAN_MRI
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
https://github.com/jongcye/RobustALOHA
Sparse + Low Rank Decomposition of Annihilating Filter-based Hankel Matrix for Impulse Noise Removal
https://github.com/jonryf/deep-learning-image-colorization-using-gan
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jonvthvn90/GitHbProject
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/joonson/yousaidthat
https://github.com/joovvhan/ecapa-tdnn
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/jooyae/NVIDIA_STYLEGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jordan-bird/synthetic-fruit-image-generator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/josephrocca/tfjs-acgan-browser-training
https://github.com/joshilp/ImageInpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/joshpc/StyledFontGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/joycex99/conditional-presgan
https://github.com/jparcill/gansbestfriend
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jpjuvo/64-3D-RaSGAN
The relativistic discriminator: a key element missing from standard GAN
https://github.com/jpowie01/DCGAN_CelebA
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/jshi31/edge-connect
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/jsll/pytorch_6dof-graspnet
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
https://github.com/json9512/sonarToimage
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/jss367/thiskangaroodoesnotexist
https://github.com/jtcass01/SIMON
https://github.com/jtchen0528/PCL-I2G
https://github.com/jtiger958/cyclegan-pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/jtiger958/pytorch-computer-vision-basic
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtiger958/pytorch-computer-vision-tutorial
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jugatsingh/self_attention_video
https://github.com/juletx/image-caption-generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.