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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/karl-hajjar/Generative-Adversarial-Networks

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

https://github.com/karlchahine/neural-cover-selection-for-image-steganography

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/kaseris/ILSVRCPlus

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kbpi314/iclust_paper

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/kclip/localized-adaptive-risk-control

Deep Residual Learning for Image Recognition

https://github.com/keillernogueira/exploit-cnn-rs

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/keras-team/keras-applications

Deep Residual Learning for Image Recognition

https://github.com/kevin851066/Deep_Learning_for_Computer_Vision

Adversarial Discriminative Domain Adaptation

https://github.com/kevinmusgrave/pytorch-adapt

Unsupervised Domain Adaptation by Backpropagation

https://github.com/khawajatalhahaseeb/Class-Activation-Map-Keras

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/khurram702/StyleBasedGAN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/khush3/GAN

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

https://github.com/kingcheng2000/GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/kingcong/gpu_FaceAttribute

Deep Residual Learning for Image Recognition

https://github.com/kingcong/gpu_SE-Net

Deep Residual Learning for Image Recognition

https://github.com/kingcong/resnet

Deep Residual Learning for Image Recognition

https://github.com/kiranu1024/deep-learning

Deep Residual Learning for Image Recognition

https://github.com/kishanAk21/Skin-Cancer-Image-Classification

Deep Residual Learning for Image Recognition

https://github.com/kishorkuttan/traffic_net

Deep Residual Learning for Image Recognition

https://github.com/kiyohiro8/pytorch-unpaired-image-to-image-translation

Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

https://github.com/km1414/CNN-models

Deep Residual Learning for Image Recognition

https://github.com/kmkwon94/ainized-ganilla-dev

GANILLA: Generative Adversarial Networks for Image to Illustration Translation

https://github.com/kmzzhang/nbi

Deep Residual Learning for Image Recognition

https://github.com/komeiharada/learn_DL_together

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/konny0311/vgg

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/konstantinos-p/image_classification_SOTA

Deep Residual Learning for Image Recognition

https://github.com/koshian2/ResNet-MultipleFramework

Deep Residual Learning for Image Recognition

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

Deep Residual Learning for Image Recognition

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

Adversarially Learned Inference

https://github.com/kuangzijian/drifu-for-animals

SiCloPe: Silhouette-Based Clothed People

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

Deep Residual Learning for Image Recognition

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