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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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코드

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

https://github.com/xcmyz/Face-Binary-Classification

Deep Residual Learning for Image Recognition

https://github.com/xcmyz/FaceDetection

Deep Residual Learning for Image Recognition

https://github.com/xcmyz/Face_Binary_Classfication

Deep Residual Learning for Image Recognition

https://github.com/xhlulu/arxiv-assistant

Deep Residual Learning for Image Recognition

https://github.com/xhujoy/CycleGAN-tensorflow

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

https://github.com/xiaoiker/meta_dpm

Taming Transformers for High-Resolution Image Synthesis

https://github.com/xiaowillow/AdaptSegNet

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/xiaowillow/AdaptSegNet1

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/xiaoyufenfei/LEDNet

Deep Residual Learning for Image Recognition

https://github.com/xingchenzhao/Generating-Human-Skeletons-with-Mutual-Actions-WGAN-Pytorch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/xingchenzhao/deep-learning-team-project

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/xinkuansong/modified-resnet-acc-0.9638-10.7M-parameters

Deep Residual Learning for Image Recognition

https://github.com/xiuyu0000/new_papers_codes/tree/main/Pix2PixHD

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/xiuyu0000/papers_with_examples/tree/main/cancer_detection_demo

Deep Residual Learning for Image Recognition

https://github.com/xiuyu0000/papers_with_examples/tree/main/cyclegan

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

https://github.com/xiuyu0000/papers_with_examples/tree/main/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/xiuyu0000/tutorials/tree/main/resnet

Deep Residual Learning for Image Recognition

https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/resnet.py

Deep Residual Learning for Image Recognition

https://github.com/xiuyu0000/vision/blob/main/mindvision/classification/models/vgg.py

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/xslidi/EfficientNets_ddl_apex

Deep Residual Learning for Image Recognition

https://github.com/xufanxiong/classification-of-caltech-256

Deep Residual Learning for Image Recognition

https://github.com/xw-hu/Mask-ShadowGAN

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

https://github.com/xxlya/CycleGAN

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

https://github.com/xypu98/two-stage-HDR-video

A Two-stage Deep Network for High Dynamic Range Image Reconstruction

https://github.com/yahoo/open_nsfw

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/EfficientNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/gaze-estimation

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/head-pose-estimation

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/retinaface-pytorch

Deep Residual Learning for Image Recognition

https://github.com/yangboz/TransferLearning4Dentist

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/yangorwell/NGPlus

Deep Residual Learning for Image Recognition

https://github.com/yangyucheng000/CycleGan

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

https://github.com/yangyucheng000/MSpaper/tree/main/U-GAT-IT

Feature Quantization Improves GAN Training

https://github.com/yangyucheng000/Pix2pix-2

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

https://github.com/yangyucheng000/resnet_Ascend

Deep Residual Learning for Image Recognition

https://github.com/yankungou/GOYA

Measuring the Biases and Effectiveness of Content-Style Disentanglement

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