https://github.com/kingsj0405/pulse-any-image
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Papers with Code 아카이브
3D Object Super-Resolution
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3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kli8996/HISR-Spectral-Mixup-Heterogeneous-Datasets
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
https://github.com/km1414/GAN
https://github.com/koi953215/NaRCan
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
https://github.com/kongdebug/RCAN-Paddle
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
https://github.com/kornia/kornia
https://github.com/korra141/Generative-Adversarial-Networks
https://github.com/krantirk/Self-Supervised-photo
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/krasserm/super-resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/kristerseiya/SinGAN_Implementation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ksivaman/super-res
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/kuleshov/audio-super-res
https://github.com/kumar-shridhar/BayesianConvNet
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch
https://github.com/kunzmi/ImageStackAlignator
https://github.com/kvsnoufal/neural_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/kwantommy/breast-cancer-diagnosis
https://github.com/kynk94/TF2-Image-Generation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/l3th4l/SuperRes
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/laomao0/BIN
https://github.com/laowng/GISR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/lavolpiana/deep-image-prior
https://github.com/lee-aaron/Image-Super-Resolution
https://github.com/leehomyc/Photo-Realistic-Super-Resoluton
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/leftthomas/SRGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/leftthomas/espcn
https://github.com/leolya/Audio-Super-Resolution-Tensorflow2.0-TFiLM
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/efficientvit
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
https://github.com/libreai/neural-painters-x
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/lightning-uq-box/lightning-uq-box
https://github.com/lilianweng/unified-gan-tensorflow
https://github.com/lilijiangg/autodiffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/linfengyu77/efkan
Fourier Neural Operator for Parametric Partial Differential Equations
https://github.com/linxi159/GAN-training-tricks
https://github.com/linxi159/Tips-and-tricks-to-train-GANs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.