https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Implicit_Neural_Representations_with_Periodic_Activation_Functions
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/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Improved_Techniques_for_Training_GANs
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Improved_Training_of_Wasserstein_GANs
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/NICE_Non_linear_Independent_Components_Estimation
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Pseudo_Numerical_Methods_for_Diffusion_Models_on_Manifolds
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unsupervised_Representation_Learning_with_Deep_Convolutional_Generative_Adversarial_Networks
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Wasserstein_GAN
https://github.com/MaxwellHeller/coffee_fraud_detection
https://github.com/Mayank57/Pokemon-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Maymaher/StackGANv2
https://github.com/Mayurji/Image-Classification-PyTorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mckinsey666/ACGAN-Anime-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Mckinsey666/Anime-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Mckinsey666/Conditional-GAN-Anime-Generation
https://github.com/Medabid1/TravelGAN
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet
https://github.com/MegEngine/Models/tree/master/official/vision/gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Meghraj-Webllisto/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/MehdiZouitine/Learning-Disentangled-Representations-via-Mutual-Information-Estimation
Learning Disentangled Representations via Mutual Information Estimation
https://github.com/MenghaoGuo/EANet
Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
https://github.com/MiX-S/resnet_cifar10
https://github.com/Michael-T-McCann/simple-WGAN
https://github.com/MichaelWangfc/distributed-tensorflow-resnet
https://github.com/MichalKacprzak99/reconstruction_particle_mass_spectra
https://github.com/Michedev/DDPM-Pytorch
https://github.com/Michel-liu/FatFormer
CNN-generated images are surprisingly easy to spot... for now
https://github.com/MicroprocessorX069/Comparison-of-DC-GANS-and-SA-GANS
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/MiloszGrabski/WGAN-gp
https://github.com/Min-Jaewon/PSLD_PAG
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-119
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mind23-2/MindCode-13
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Mind23-2/MindCode-171
https://github.com/Mind23-2/MindCode-18
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.