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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/rvorias/uvHolographics

Learning from Simulated and Unsupervised Images through Adversarial Training

https://github.com/ry/tensorflow-resnet

Deep Residual Learning for Image Recognition

https://github.com/ryanwng12/img-colorization

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/sagnik1511/Transfer-Learning-with-Python

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sahilg06/BicycleGAN

Toward Multimodal Image-to-Image Translation

https://github.com/sahilunagar/VGG16-model

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/saikrishnadas/ResNet-Pytorch

Deep Residual Learning for Image Recognition

https://github.com/saimj7/Action-Recognition-in-Real-Time

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/saloni1998/Cifar-10

Deep Residual Learning for Image Recognition

https://github.com/samarthshukla/ws-i2i

Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation

https://github.com/samb-t/unleashing-transformers

Taming Transformers for High-Resolution Image Synthesis

https://github.com/sammyamajumdar/ResNet50

Deep Residual Learning for Image Recognition

https://github.com/samuelchassot/FUNIT

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sangHa0411/CIFAR100

Deep Residual Learning for Image Recognition

https://github.com/sangHa0411/ResNetCIFAR100

Deep Residual Learning for Image Recognition

https://github.com/sangdon/pac-ps-w

Domain-Adversarial Training of Neural Networks

https://github.com/sanghyun-son/clustering-kernels

Deep Residual Learning for Image Recognition

https://github.com/sangyun884/CinCGAN-pytorch

Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks

https://github.com/sangyun884/Face2Webtoon

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/sangyun884/WebtoonFaces

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/sangyun884/fast-ode

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow

https://github.com/sanjsvk/CycleGANs

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sanoyo/dl-model

Deep Residual Learning for Image Recognition

https://github.com/sarthakbaiswar/Artistic-Styling

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/saviaga/faceswap

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/say2sarwar/DeepAcid

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/sayakpaul/Adaptive-Gradient-Clipping

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/scarlettjiali/Tools_Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/scpark20/universal-music-translation

Domain-Adversarial Training of Neural Networks

https://github.com/sdnr1/c-gan_pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/seansoleyman/cifar10-resnet

Deep Residual Learning for Image Recognition

https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN

Deep Residual Learning for Image Recognition

https://github.com/sebsquire/FlowersRecognition_keras

Deep Residual Learning for Image Recognition

https://github.com/seishinkikuchi/test

Deep Residual Learning for Image Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.