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

https://github.com/yashclone999/ResNet_MODEL

Deep Residual Learning for Image Recognition

https://github.com/yaxingwang/UDIT

Multimodal Unsupervised Image-to-Image Translation

https://github.com/ycao5602/SAL

Deep Residual Learning for Image Recognition

https://github.com/yczhang1017/SSD_resnet_pytorch

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/yd8534976/conGAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/yehuitang/Pruning

Co-Evolutionary Compression for Unpaired Image Translation

https://github.com/yenchenlin/pix2pix-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/yeshwanth69/SPADE

Semantic Image Synthesis with Spatially-Adaptive Normalization

https://github.com/yex666/cyclegan-in-mindspore

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

https://github.com/yibeiou/Advanced_Business_Intelligence

Deep Residual Learning for Image Recognition

https://github.com/yjzhai-cs/vehicle-10

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/yoshitomo-matsubara/torchdistill

Deep Residual Learning for Image Recognition

https://github.com/youdao-ai/SRNet

Editing Text in the Wild

https://github.com/youwenjing/reid_mgn-dgnet

Joint Discriminative and Generative Learning for Person Re-identification

https://github.com/yoyotv/Image-derain-via-CGAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/yqyao/ssd.pytorch

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/yuan-zm/dgt-st

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

https://github.com/yudhisteer/Face-Recognition-with-Masks

Deep Residual Learning for Image Recognition

https://github.com/yunjey/StarGAN

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/yunjey/domain-transfer-network

Unsupervised Cross-Domain Image Generation

https://github.com/yunjey/mnist-svhn-transfer

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

https://github.com/yunxiangfu2001/dreamda

Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance

https://github.com/yuvrajarora/CSCI5922_Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/yuzhenmao/MI_P2V

MCMI: Multi-Cycle Image Translation with Mutual Information Constraints

https://github.com/yw0nam/DenseNet

Deep Residual Learning for Image Recognition

https://github.com/yxgeee/MMT

Deep Residual Learning for Image Recognition

https://github.com/yxue3357/CycleGAN_pt

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

https://github.com/yxue3357/CycleGAN_tf

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

https://github.com/yxue3357/Mask-CycleGAN

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

https://github.com/yyk-wew/F3Net

FaceForensics++: Learning to Detect Manipulated Facial Images

https://github.com/zcemycl/Matlab-GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/zeka-io/selfi-to-anime

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

https://github.com/zeka-io/selfie-to-anime

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

https://github.com/zhanglichao/end2end_rgbt_tracking

Multi-Modal Fusion for End-to-End RGB-T Tracking

https://github.com/zheng-ningxin/Pruning-from-scratch

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/zhengziqiang/P2C

Unpaired Photo-to-Caricature Translation on Faces in the Wild

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