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/CW-Huang/sdeflow-light
https://github.com/CXH-Research/UIE-UnFold
https://github.com/Caimthefool/Paddle_T2I
https://github.com/Callifrey/ACGAN-Paddle
https://github.com/CanshangD/ResNet_tensorflow2.0
https://github.com/Carl0520/ADDA-pytorch
https://github.com/Carl0520/DANN_pytorch-
https://github.com/CarpdiemLiang/style_transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/CasualGANPapers/Make-A-Scene
Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
https://github.com/Cathy-t/HELLO_image
https://github.com/CatoGit/Comparing-the-Performance-of-Deepfake-Detection-Methods-on-Benchmark-Datasets
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
https://github.com/CellSMB/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Chad4545/styletransfer
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/ChandhiniG/Generate-Stylized-Image-from-Edges
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models
https://github.com/Chemino/UGATIT-Paddle
https://github.com/ChenDelong1999/VirtualConductor
VirtualConductor: Music-driven Conducting Video Generation System
https://github.com/ChengBinJin/DCGAN-TensorFlow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ChengBinJin/MRGAN-TensorFlow
https://github.com/ChengBinJin/SpineC2M
https://github.com/ChengBinJin/WGAN-GP-tensorflow
https://github.com/ChengBinJin/WGAN-TensorFlow
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/ChihchengHsieh/AttnGAN_Implementation
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/Chinmay26/Multi-Viewpoint-Image-generation
Transformation-Grounded Image Generation Network for Novel 3D View Synthesis
https://github.com/Chloejay/image_caption_app
https://github.com/ChopsticksAN/generative_inpainting
https://github.com/ChrisAllenMing/Mixup_for_UDA
https://github.com/ChristophReich1996/Dirac-GAN
https://github.com/ChristophReich1996/Mode_Collapse
https://github.com/ChristophReich1996/Multi-StyleGAN
https://github.com/ChristophReich1996/Semantic_Pyramid_for_Image_Generation
https://github.com/ClaraBing/flow
https://github.com/Clemson-AI/p2gan
P$^2$-GAN: Efficient Style Transfer Using Single Style Image
https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest
https://github.com/CodeChefVIT/VOID
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.