https://github.com/Colin97/OpenShape_code
OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understanding
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/CompPhysVienna/paper_NF_for_rare_events
https://github.com/CompVis/content-targeted-style-transfer
https://github.com/CompVis/iin
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
https://github.com/CompVis/unsupervised-part-segmentation
Unsupervised Part-Based Disentangling of Object Shape and Appearance
https://github.com/Con6924/SPM
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
https://github.com/Coral79/CDDB
Incremental learning for the detection and classification of GAN-generated images
https://github.com/CorneliusHsiao/FoodMethodGAN
https://github.com/CoryRJ/cycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/CreativeCodingLab/DeepIllumination
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/CtrlZ1/Domain-Adaptation-Algorithms
https://github.com/CtrlZ1/Domain-Adaptive-CodeBase
https://github.com/CuddleSabe/VQGAN
https://github.com/D-X-Y/ResNeXt-DenseNet
https://github.com/D-hash-code/ffjord-rnode-finalweek-mnist
How to train your neural ODE: the world of Jacobian and kinetic regularization
https://github.com/DAA233/generative-inpainting-pytorch
https://github.com/DDCM-2025/ddcm-compressed-image-generation
Compressed Image Generation with Denoising Diffusion Codebook Models
https://github.com/DELTA37/LatentKeypointGAN
https://github.com/DLHacks/pix2pix_PAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/DMarwan/NutricIA
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DaVran369/face
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/Daeijavad/MAD-GAN
https://github.com/DaikiTanak/manifold_mixup
https://github.com/DakshIdnani/pytorch-nice
https://github.com/DanGOTO100/SCADA-GAN-Synthetic-Generation
https://github.com/Daniahblog/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Daniel-H-99/CRD
https://github.com/Daniel-H-99/SIREN
Implicit Neural Representations with Periodic Activation Functions
https://github.com/DanielLongo/AdversarialTrain
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/DanielLongo/GANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/DanielLongo/cGANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/DanielaGall/AI-CHALLENGE2
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/Daniil-Selikhanovych/ebm-wgan
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.