https://github.com/carpedm20/DCGAN-tensorflow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/carpedm20/simulated-unsupervised-tensorflow
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/carzit/sd-webui-samplers-scheduler
Elucidating the Design Space of Diffusion-Based Generative Models
https://github.com/casey-barr/generative-models-in-tensorflow
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/cassiePython/cddfm3d
Cross-Domain and Disentangled Face Manipulation with 3D Guidance
https://github.com/catalyst-team/gan
https://github.com/catjacks38/FCC-GAN
FCC-GAN: A Fully Connected and Convolutional Net Architecture for GANs
https://github.com/cbhower/style-gan-music-video
https://github.com/cbritom/GAN
https://github.com/cc-hpc-itwm/DeepFakeDetection
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/cedricoeldorf/ConditionalStyleGAN
https://github.com/celdeldel/style_conditionnal_gan
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/cene555/Imagen-pytorch
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
https://github.com/center-for-health-data-science/dgd_paper
https://github.com/cffo/stylegan2-encoder
https://github.com/cfinlay/ffjord-rnode
How to train your neural ODE: the world of Jacobian and kinetic regularization
https://github.com/cfl0122/face
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/ch10tang/stylegan2-based-face-frontalization
https://github.com/chail/anyres-gan
https://github.com/chail/latent-composition
Using latent space regression to analyze and leverage compositionality in GANs
https://github.com/chail/patch-forensics
What makes fake images detectable? Understanding properties that generalize
https://github.com/chameleonTK/continual-learning-for-HAR
https://github.com/chandlersupple/Tensorflow-PixelCNN
https://github.com/chandragupta0001/GAN/tree/master/info_gan
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/changwoo-00/Adversarial_Latent_Autoencoders
https://github.com/changwoolee/WGAN-GP-tensorflow
https://github.com/chanshing/geocondition
https://github.com/chanshing/sobolev_gan
https://github.com/chao-ji/image-translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/charan223/Brain-Tumor-Segmentation-using-Topological-Loss
https://github.com/charan223/brain_tumor_topology
https://github.com/charan223/topology-aware-networks
https://github.com/charan223/topology-conscious-networks
https://github.com/charchit7/QuickDraw-App
https://github.com/charlescheng0117/p2pvg
https://github.com/charlescheng0117/pytorch-WGAN-GP-TTUR-CelebA
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.