https://github.com/HongtengXu/Relational-AutoEncoders
Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/HongyangGao/PixelTCN
https://github.com/Hosein47/LOGAN
LOGAN: Latent Optimisation for Generative Adversarial Networks
https://github.com/Hramchenko/diffusion_distiller
Progressive Distillation for Fast Sampling of Diffusion Models
https://github.com/HsiehKang/wgan/blob/main/wgan.ipynb
https://github.com/HsinYingLee/MDMM
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/Hydrino/ACGAN_cifar10
https://github.com/Hydrino/ACGAN_mnist
https://github.com/HyelinNAM/ContrastiveDenoisingScore
Contrastive Denoising Score for Text-guided Latent Diffusion Image Editing
https://github.com/IBM/MAX-Audio-Sample-Generator
https://github.com/IBM/NeuronAlignment
https://github.com/IEWbgfnYDwHRoRRSKtkdyMDUzgdwuBYgDKtDJWd/doldy
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset
Conditional Image Generation and Manipulation for User-Specified Content
https://github.com/IIGROUP/TediGAN
https://github.com/IMvision12/DCGAN-Tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/IMvision12/keras-vision-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/INOS-soft/AI
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/Bro-coil
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/KuE.electo-waffle
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/INOS-soft/Python-Tools
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/IPL-UV/gauss4eo
https://github.com/IPL-UV/rbig
https://github.com/IPL-UV/rbig_matlab
https://github.com/IShengFang/SpectralNormalizationKeras
https://github.com/Ian-Liao/LoBCoD
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model
https://github.com/Iceland-Leo/Style-MSG-GAN
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
https://github.com/Ichi-Mark-Kate/Cowboy-StyleGAN-2
https://github.com/Ierezell/PapierFewShot
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/Iheb96/SinGAN_InPainting
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/IjustAi/smartbrush
SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model
https://github.com/ImagingLab/Colorizing-with-GANs
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Intelligent-Imaging-Center/Spectral-Reconstruction
https://github.com/InterDigitalInc/Latent-Transformer
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/InternityFoundation/SemanticInpainting
https://github.com/Ipsedo/MusicDiffusion
https://github.com/Ipsedo/MusicGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.