Papers with Code 아카이브
1 Image, 2*2 Stitching
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/DavidCastilloAlvarado/U-NET_AUTOENCODER
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DeepMIALab/AI-FFPE
Contrastive Learning for Unpaired Image-to-Image Translation
https://github.com/DeepMark/deepmark
https://github.com/DeepikaKaranji/CycleGAN-Unpaired-Image-translation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Demonhesusheng/Reid
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/Deopoler/DCLGAN-Keras
Dual Contrastive Learning for Unsupervised Image-to-Image Translation
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/Dominioncher/smart-sketch
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/E-DEEP/PapersReview
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EiroaMD/final_project_IH_pneumonia
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Electro1111/COVID_19_CXR_CLASSIFICATION
https://github.com/Elman295/Maximum-Classifier-Discrepancy-for-Unsupervised-Domain-Adaptation
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/Elman295/Unsupervised-Domain-Adaptation-by-Backpropagation-
https://github.com/Emir-Liu/Cycle-GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/EngineeringSapien/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EricAlcaide/MiniFold
https://github.com/EstherBear/implementation-of-pruning-filters
https://github.com/EverLookNeverSee/diag2model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/EvgenyVendrov/DeepLearningCourse
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Eyyub/tensorflow-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/FHomps/ADViSE
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/FaceOnLive/DeepFake-Detection-SDK-Linux
https://github.com/Feynman1999/MgeEditing
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/FiveEyes/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/FrancescoMarchesini/pixToPix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/FrancescoSaverioZuppichini/ResNet
https://github.com/Fujiki-Nakamura/ADDA.PyTorch
https://github.com/Funaizhang/dics
Improving Shape Deformation in Unsupervised Image-to-Image Translation
https://github.com/GBATZOLIS/Wespe-Keras
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.