Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/ShigemichiMatsuzaki/MSPL
https://github.com/Shivamt99/Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ShnitzelKiller/generative_inpainting
https://github.com/ShreyasArthur/StyleGAN-2-with-Urban-Plans
https://github.com/Shumway82/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN
https://github.com/Shuvrajit9904/PairedCycleGAN-tf
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/ShwanMario/ASWD
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/SiavashCS/sgan_simple
https://github.com/Siddhartha24795/Medical-Image-Synthesis
https://github.com/SilentFalls/tog
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
https://github.com/SilverEngineered/WaveGan
https://github.com/SimRunBot/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/SimeonKraev/Musical-style-transfer-with-cycle-consistent-generative-adversarial-neural-networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SimonDele/School-projects
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/SimoneDutto/EDSR
https://github.com/SinclairHudson/RePaint
https://github.com/Sinestro38/qosf-qgan
https://github.com/SiskonEmilia/Anime-Wifu-Dataset
Towards the Automatic Anime Characters Creation with Generative Adversarial Networks
https://github.com/SiskonEmilia/StyleGAN-PyTorch
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Slimane33/OTGAN
https://github.com/Smilels/multimodal-translation-teleop
A Mobile Robot Hand-Arm Teleoperation System by Vision and IMU
https://github.com/SnailDev/github-hot-hub
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/Solacex/CCM
https://github.com/Soldie/DeOldify-colorir-imagens-antigas
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/Soldie/insightface-Rec.Face
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/SongDark/generate_normal
https://github.com/Sontref/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SonyCSLParis/DrumGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/SoonminHwang/rgbt-ped-detection
https://github.com/Sorananakiii/Fine-grained-image-recognition
https://github.com/Soumya1612-Rasha/Keras-GAN_rasha
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/SoumyadeepJana/CelebrityFaceGeneration
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.