https://github.com/maxencealluin/mnist_DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Papers with Code 아카이브
Image Generation
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- 평가표
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- 보존된 평가 기록
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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/maxjcohen/diffusion-bridges
https://github.com/maxmax1992/WGAN_WGAN-GP
https://github.com/maxwelltsai/DeepGalaxy
https://github.com/mayuelala/followyourcanvas
Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videos
https://github.com/maz0318/nlpSummerCamp2019
https://github.com/mbarbetti/tf-gen-models
https://github.com/mbbrodie/stylegan2
https://github.com/mbinkowski/DeepSpeechDistances
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/mbordyuh/Contrastive_Divergence
Improved Contrastive Divergence Training of Energy Based Models
https://github.com/mbordyuh/ebm
Improved Contrastive Divergence Training of Energy Based Models
https://github.com/mbordyuh/score_matching
Generative Modeling by Estimating Gradients of the Data Distribution
https://github.com/mbsariyildiz/resnet-pytorch
https://github.com/mcaseyrehm/pix2pixColab
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mcclow12/wgan-gp-pytorch
https://github.com/mcd4874/neurips_competition
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/mchiappe83/Generate_Faces
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/mdda/deep-learning-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/meg965/pytorch-rl
https://github.com/megvii-research/basecls/tree/main/zoo/public/resnet
https://github.com/megvii-research/basecls/tree/main/zoo/public/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/megvii-research/conr
https://github.com/megvii-research/ijcai2023-conr
https://github.com/mehdibnc/Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mehdidc/any2any
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mehdidc/hybrid-keras
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/memesoo99/regan
https://github.com/mengfu188/insightface.bak
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/menloresearch/ichigo
https://github.com/menon92/Python-Learning
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/menyifang/adgan
Controllable Person Image Synthesis with Attribute-Decomposed GAN
https://github.com/mevius6/smallgan
Image Generation From Small Datasets via Batch Statistics Adaptation
https://github.com/mgmk2/StyleGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/mgonzalezrivero/reef_learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mhagiwara/nanigonet
https://github.com/mhuse/StyleGAN2LatentspaceInterpolation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.