https://github.com/yuzhenmao/MI_P2V
MCMI: Multi-Cycle Image Translation with Mutual Information Constraints
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/yw0nam/DenseNet
https://github.com/yxgeee/MMT
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/zcemycl/Matlab-GAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/zeka-io/selfi-to-anime
https://github.com/zeka-io/selfie-to-anime
https://github.com/zhanglichao/end2end_rgbt_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
https://github.com/zhenxingjian/partial_distance_correlation
Measuring the Biases and Effectiveness of Content-Style Disentanglement
https://github.com/zhenxuan00/graphical-gan
https://github.com/zhouZhouYUI/UGATIT_paddle
https://github.com/zhuangpeiyu/reloc
Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis
https://github.com/zhuo931077127/CycleGAN_practice
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/zjZSTU/ResNet
https://github.com/zjsong/CDNet
https://github.com/zjsong/sspl
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/znxlwm/pytorch-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/zqwhu/SegDAwithBoundary
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/zsano1/MEnet-FCN-wild
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
https://github.com/zuobinxiong/pix2pix-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/zzz2010/starganv2_paddle
https://gitlab.com/birder/birder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://gitlab.com/nct_tso_public/surgical-video-sim2real
https://gitlab.com/williamsdaniel888/valma
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.