Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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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/IrvingMeng/MagFace
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/IsHYuhi/Image_Inpainting_Using_Partial_Convolutions
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/IsaacPatole/DCGAN-and-CGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/J-woooo/Mask_Remover
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/JCBrouwer/maua-stylegan2
https://github.com/JContro/pytorch_projects
https://github.com/JHA-Lab/impromptu
https://github.com/JHKim-snu/GVCCI
https://github.com/JHKim-snu/PGA
https://github.com/JHLee0513/Salt_detection_challenge
https://github.com/JHpark1677/CGAN-DDLS
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
https://github.com/JL321/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JONGSKY/paper
https://github.com/JPlin/MSGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/JackMcCoy/riemann-noise-pytorch
https://github.com/JackonLiu/SinGAN-Easy
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/JaidedAI/EasyOCR
https://github.com/James-Gilbert-/medical-deep-learning
https://github.com/James-Gilbert-/pneumonia-detection
https://github.com/JanMarcelKezmann/Residual-Network-Architectures
https://github.com/Janspiry/Image-Super-Resolution-via-Iterative-Refinement
https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models
https://github.com/JaryV/CycleGAN_OldYoung
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Jason-CKY/pokeGAN_fusion
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Jay0505/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/JayPatwardhan/ResNet-PyTorch
https://github.com/JeansonYang/VGG16-MindSpore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Jeff860530/ADDA
https://github.com/JennyVanessa/Deepfill-Paddle
https://github.com/Jentuuh/edge-preserving-diffusion
https://github.com/JeongHyunJin/Pix2PixHD
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/Jeongyun-Lee-0423/Classification
https://github.com/JesonZhang822/Dogs-VS-Cats-Project
https://github.com/JeyesHan/P2LR
https://github.com/Jhonve/jhonve.github.io
DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.