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/sangHa0411/ResNetCIFAR100
https://github.com/sangdon/pac-ps-w
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sangyun884/CinCGAN-pytorch
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
https://github.com/sangyun884/Face2Webtoon
https://github.com/sangyun884/WebtoonFaces
https://github.com/sangyun884/fast-ode
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/sanjsvk/CycleGANs
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sanoyo/dl-model
https://github.com/sarthakbaiswar/Artistic-Styling
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/saviaga/faceswap
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/say2sarwar/DeepAcid
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/scarlettjiali/Tools_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/scpark20/universal-music-translation
https://github.com/sdnr1/c-gan_pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/seansoleyman/cifar10-resnet
https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN
https://github.com/sebsquire/FlowersRecognition_keras
https://github.com/seishinkikuchi/test
https://github.com/semi0612/cat_and_dog_vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/septmars/DL
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/seqam-lab/GPDA
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
https://github.com/sergeisoly/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/serre-lab/gala_tpu
https://github.com/seyrankhademi/ResNet_CIFAR10
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shao-chi/ImageCaption
https://github.com/shaoanlu/fewshot-face-translation-GAN
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/shaominghe/stargan_adience
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/shijianjian/efficientnet-pytorch-3d
https://github.com/shikhadahiya/Image-to-image-translation-using-C-GAN
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/shinseung428/simGAN_NYU_Hand
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/shivammehta25/Matcha-TTS
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.