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/JuanCasian/ML-Speech-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JuliaWolleb/diffusion-anomaly
https://github.com/Justin-Tan/generative-compression
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/KU-CVLAB/MIDMs
Cross-domain Correspondence Learning for Exemplar-based Image Translation
https://github.com/KaimingHe/deep-residual-networks
https://github.com/Kal213/StarGAN-Tutorial-Tensorflow-2.3
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/Kalyan0821/NST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kalyan0821/myelinfoundry-challenge
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarT1994/EmnistRecognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarimaCha/CNN_Trust_Prediction
https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs
https://github.com/KbeautyHair/BaselineModel
https://github.com/KeiraZhao/MDAN
https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10
https://github.com/KellyHwong/rethinking_generalization
https://github.com/King-Otaku/Emotion_Recognition_DNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kokonut133/MagicDraw
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/Kokonut133/frame2frame
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/KookHoiKim/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/Kprerak-iisc/data-science
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/KrishnaswamyLab/travelgan
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
https://github.com/LKLQQ/StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/LKLQQ/ssc_resnet50
https://github.com/Leinadh/avatar-image-generator
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/Lester1711/DSI15_Capstone
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiYuhangUSTC/Sketch2Face
https://github.com/Little-Frog-233/imagecluster
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiuNull/pix2pix_Liu
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/LoganOneal/neuralpaint-server
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/Lornatang/CoGAN-PyTorch
https://github.com/Lornatang/PyTorch-CycleGAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Lornatang/ResNet-PyTorch
https://github.com/Lornatang/tensorflow-vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Lotayou/CoCosNet
Cross-domain Correspondence Learning for Exemplar-based Image Translation
https://github.com/Lotayou/everybody_dance_now_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.