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/koshian2/ResNet-MultipleFramework
https://github.com/koshian2/SNGAN
https://github.com/koshian2/swd-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/kostagiolasn/Unsupervised-Discovery-of-Semantic-Concepts-in-Satellite-Imagery-with-Style-based-Wavelet-driven-Gen
https://github.com/kostagiolasn/satconcepts
https://github.com/kousw/experimental-consistory
https://github.com/kozistr/gan-metrics
Reliable Fidelity and Diversity Metrics for Generative Models
https://github.com/kozistr/improved-edge-connect
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/kpandey008/DCGANS
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/kpandey008/SAGAN
https://github.com/krantirk/-Adversarial-Latent-Autoencoders
https://github.com/kregmi/cross-view-image-matching
Cross-view image synthesis using geometry-guided conditional GANs
https://github.com/kreimanlab/HumanIntentionInferenceZeroShot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/krishnakarthi/COVID-19_Prediction
https://github.com/kristerseiya/SinGAN_Implementation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/kroniidvul/LostGANs-mnist
https://github.com/krumo/Detectron-DA-Faster-RCNN
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/krumo/Domain-Adaptive-Faster-RCNN-PyTorch
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/kryvosheyev/xray-anomaly-detection
https://github.com/ksagit/sn_gan_pytorch
https://github.com/kthksgy/pytorch-template
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/ktjonsson/keras-ArcFace
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/kuangshengkai/dcgan-keras
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/kuangzijian/drifu-for-animals
https://github.com/kubantjan/fast-form
TextCaps : Handwritten Character Recognition with Very Small Datasets
https://github.com/kulkarnikeerti/Image-to-image-translation-using-cGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/kushalpatil1997/text_to_image_synthesis
https://github.com/kw01sg/crda
https://github.com/kwai-kolors/kolors
Learning Multi-dimensional Human Preference for Text-to-Image Generation
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyegomez/CM3Leon
Scaling Autoregressive Multi-Modal Models: Pretraining and Instruction Tuning
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/kylelo/roofdiffusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.