https://github.com/bardank/dc-gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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/bareeka/ganproject
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/barisbatuhan/SSuperGAN
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
https://github.com/barmayo/spatial_attention
https://github.com/bartoszpanek1/Real-time-emotion-recognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/basilevh/image-outpainting
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/basiralab/MultiGraphGAN
https://github.com/basiralab/topoGAN
https://github.com/bayraktarbaris/SNGAN
https://github.com/bbeatrix/introvae
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
https://github.com/bbzhu-jy16/DomainStudio
https://github.com/bbzhu-jy16/MaskDis
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/bcmi/F2GAN-Few-Shot-Image-Generation
https://github.com/bcmi/MatchingGAN-Few-Shot-Image-Generation
https://github.com/bcmi/Object-Shadow-Generation-Dataset-DESOBA
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/bcmi/ObjectStitch-Image-Composition
Making Images Real Again: A Comprehensive Survey on Deep Image Composition
https://github.com/bcmi/mureobjectstitch-image-composition
Making Images Real Again: A Comprehensive Survey on Deep Image Composition
https://github.com/bdehaine/SinGan
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/ben-davidson-6/fixup
https://github.com/benMen87/SparseLearningListaProject
https://github.com/benearnthof/fm_boosting
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/benguk46/StyleGAN2
https://github.com/bennyqp/artificial-inspiration
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/bentrevett/pytorch-image-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/bepierre/SpeechVGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/beresandras/buggy-resizing-critique
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/beresandras/clear-diffusion-keras
https://github.com/beresandras/gan-flavours-keras
https://github.com/bermudezjose/SAR2Optical-using-cGANS
https://github.com/bertrandlalo/deep_dog_breeds
https://github.com/bethgelab/InDomainGeneralizationBenchmark
https://github.com/bgtripp/stylegan2glacier
https://github.com/bhargavajs07/Packed-Wasserstein-GAN-with-GradientPenalty-Example
https://github.com/bhargavajs07/Packed_WGAN_GP_Example
https://github.com/bigai-nlco/cream
AR-Diffusion: Auto-Regressive Diffusion Model for Text Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.