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/RuiLiFeng/noise
https://github.com/RuoyuGuo/Visualising-Image-Classification-Models-and-Saliency-Maps
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/RuqiBai/mixture_flow
https://github.com/Rust401/pixel2piexl
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Ruturaj123/VGG16-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ryan0v0/Google_ML_Camp
Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
https://github.com/RyanWu2233/Style_GAN2_FFHQ
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/RyanWu2233/Style_GAN2_TWDNE
https://github.com/RyotaImai/-Keras-Image2StyleGAN-
https://github.com/S-aiueo32/dni-demo
Deep Network Interpolation for Continuous Imagery Effect Transition
https://github.com/SAGNIKMJR/MetaQNN_ImageGenerationGAN_DiscriminatorFixed_PyTorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SHAHBAZSIDDIQI/DataScience
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SHI-Labs/Neighborhood-Attention-Transformer
https://github.com/SJHNJU/Gated_PixelCNN
https://github.com/SJHNJU/gated-pixelcnn
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SSN15/Traffic-sign-recognition-using-deep-learning-and-computer-vision
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/SSRSGJYD/NeuralTexture
Deferred Neural Rendering: Image Synthesis using Neural Textures
https://github.com/STASYA00/UrbanGen
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/STVIR/seg2vid
https://github.com/STomoya/ptfid
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/SUPERSHOPxyz/stylegan3-gradient
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Sainzerjj/Free-Guidance-Diffusion
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-Model-Builder-KERAS
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Sakib1263/VGG-1D-2D-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-KERAS
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Sakib1263/VGG-Model-Builder-Tensorflow-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.