Papers with Code 아카이브
Image Generation
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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/cjf8899/ReMix_Pytorch
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/tree/main/Diffusion/DDIM
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/tree/main/Diffusion/DDPM
https://github.com/cjpurackal/sg2im-tf
https://github.com/claCase/NormalizingFlow
https://github.com/claim-berlin/3d_stylegan_circle_of_willis
https://github.com/clarken92/vfm
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/claserken/SISS
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/claushofmann/GAN-game-art
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/clementchadebec/benchmark_VAE
Learning to Generate Images with Perceptual Similarity Metrics
https://github.com/cleye/FUNIT-Fringe
https://github.com/clic-lab/chalet
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/clic-lab/drif
Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction
https://github.com/clinicalml/parametric-robustness-evaluation
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
https://github.com/clintonjwang/controlnet
https://github.com/cloneofsimo/consistency_models
Progressive Distillation for Fast Sampling of Diffusion Models
https://github.com/cloneofsimo/lora
https://github.com/cloneofsimo/paint-with-words-sd
eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers
https://github.com/cloudwaysx/cyclegan-tensorflow_pixie
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/clovaai/fewshot-font-generation
Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
https://github.com/clu0/unet.cu
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/clvrai/ACGAN-PyTorch
https://github.com/clvrai/representation-learning-by-learning-to-count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cocktailpeanutlabs/differential-diffusion-ui
https://github.com/cocopambag/ResNet
https://github.com/code-implementation1/Code3/tree/main/FaceAttribute
https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir
https://github.com/code-implementation1/Code6/tree/main/PGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/code-implementation1/Code6/tree/main/Pix2Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/code-implementation1/Code7/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/code-implementation1/Code7/tree/main/resnetv2
https://github.com/code-implementation1/Code8/tree/main/SPADE
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/code-implementation1/Code9/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/code-trip/insightface-mxnet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.