https://github.com/TheStageAI/TorchIntegral
Implicit Neural Representations with Periodic Activation Functions
Papers with Code 아카이브
Image Generation
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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/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/ThomasRobertFr/deep-learning-figures
DualDis: Dual-Branch Disentangling with Adversarial Learning
https://github.com/Tian-Jiang/ModularGAN
https://github.com/TianxiangMa/MUST-GAN
MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation
https://github.com/TilakD/Image-Captioning
https://github.com/TimoKuenstle/timeseries
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/TinDang97/face_recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Tinysqua/VTGAN-pytorch-version
VTGAN: Semi-supervised Retinal Image Synthesis and Disease Prediction using Vision Transformers
https://github.com/TomokiKomiya/SRGAN-keras
https://github.com/Tools4Project/4501Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Touyuki/Cover_generation
Specifying Object Attributes and Relations in Interactive Scene Generation
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/TreB1eN/InsightFace_Pytorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/TrinhQuocNguyen/Edited_PConv-Keras
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/TsinghuaC3I/Efficient-Diffusion-Models
Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices
https://github.com/UBC-Computer-Vision-Group/DwNet
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/UKPLab/coling18-multimodalSurvey
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/UdbhavPrasad072300/GANs-Implementations
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/UdonDa/StarGAN-v2-pytorch-nonofficial
https://github.com/Uemuet/-gan
https://github.com/Uemuet/gan2
https://github.com/Uemuet/imet
https://github.com/Uemuet/imet2
https://github.com/Uemuet/stylegan2
https://github.com/Uemuet/stylegan22
https://github.com/UnofficialJuliaMirror/DeepMark-deepmark
https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark
https://github.com/VEDANTGHODKE/FFHQ-Ageing-Dataset
MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/VIRUS-ATTACK/DCGAN-GPU-IMPLEMENTATION
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/VIRUS-ATTACK/DCGAN-implementation.
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/VITA-Group/AutoGAN
AutoGAN: Neural Architecture Search for Generative Adversarial Networks
https://github.com/VITA-Group/Ultra-Data-Efficient-GAN-Training
https://github.com/VLL-HD/conditional_invertible_neural_networks
Guided Image Generation with Conditional Invertible Neural Networks
https://github.com/VLL-HD/exact_information_bottleneck
Guided Image Generation with Conditional Invertible Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.