https://github.com/xypu98/two-stage-HDR-video
A Two-stage Deep Network for High Dynamic Range Image Reconstruction
Papers with Code 아카이브
Image-to-Image Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 11
- 평가표
- 38
- 보존된 평가 기록
- 195
Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
<span style="color:grey; opacity: 0.6">( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yahoo/open_nsfw
https://github.com/yakhyo/EfficientNet-PyTorch
https://github.com/yakhyo/gaze-estimation
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
https://github.com/yangboz/TransferLearning4Dentist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yangorwell/NGPlus
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
https://github.com/yangyucheng000/Pix2pix-2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yankungou/GOYA
Measuring the Biases and Effectiveness of Content-Style Disentanglement
https://github.com/yashclone999/ResNet_MODEL
https://github.com/yaxingwang/UDIT
https://github.com/ycao5602/SAL
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
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
https://github.com/yjzhai-cs/vehicle-10
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/youdao-ai/SRNet
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
https://github.com/yunjey/StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/yunjey/domain-transfer-network
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.