https://github.com/code-implementation1/Code9/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
Papers with Code 아카이브
Image-to-Image Translation
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- 평가표
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- 보존된 평가 기록
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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/corenel/pytorch-adda
https://github.com/cornell-zhang/allo-pldi24-artifact
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cosmic119/StarGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/criteo-research/pytorch-ada
https://github.com/crosszamirski/guided-i2i
https://github.com/cryu854/ArbitraryStyle
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/ArbitraryStyle-tfjs
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cryu854/CUT
https://github.com/curioserve/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/cuteyyt/ImageSteganography
RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
https://github.com/cvfx-2019/homework1-color-transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cvp19g2/cvp19g2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/cydessole/AnimeLife
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/daimonae/Fundus-Image-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/daintlab/ct-denoising
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/dakenan1/Everybody-Dance-Now
https://github.com/damo-vilab/videocomposer
Composer: Creative and Controllable Image Synthesis with Composable Conditions
https://github.com/danielamassiceti/CCA-visualdialogue
https://github.com/danielparada1/RL_SpaceInvaders
https://github.com/dantecomedia/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/darrishabh/coviprox
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/darth-c0d3r/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/datawiz1984/MLND_Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datawiz1984/malaria-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datoboat/Vehicle-Detection
https://github.com/dbash/CycleGAN3D
https://github.com/dbash/visda2022-org
https://github.com/dead-mazai/avia-missing-airplanes
https://github.com/deepakprabakar96/DTN
https://github.com/deepmind/dm-haiku
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
https://github.com/deepware/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/delta6189/AttentionGAN
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/demul/ResidualNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.