https://github.com/ilyak93/SinGan
SinGAN: Learning a Generative Model from a Single Natural Image
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/ilyak93/SinGanF2
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/image-science-lab/OneNet
One Network to Solve Them All --- Solving Linear Inverse Problems using Deep Projection Models
https://github.com/imkoushik22/Walmart-Hackathon
https://github.com/inkplatform/InfoGAN-PyTorch
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/innat/External-Attention-TensorFlow
Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks
https://github.com/innat/ML-Bookmarks
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/innat/ML-Resource
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/instantx-research/instantid
InstantID: Zero-shot Identity-Preserving Generation in Seconds
https://github.com/interdigitalinc/featurestyleencoder
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/interestingzhuo/foodretreival
https://github.com/intuitive-robots/beso
Score-Based Generative Modeling through Stochastic Differential Equations
https://github.com/ipc-lab/deepjscc-diffusion
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/iphysresearch/DataSciComp
https://github.com/irynakostyshyn/A-Style-Based-Generator-Architecture-for-GAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/is90057/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/isaaccorley/deep-image-harmonization-pytorch
https://github.com/isaacschaal/SG_training
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/ispamm/realnvp-demo-pytorch
https://github.com/isrugeek/semcolour
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/itijyou/ademxapp
https://github.com/itsayushthada/Text-To-Image
https://github.com/itsss/ganimorph
Improving Shape Deformation in Unsupervised Image-to-Image Translation
https://github.com/itsuki8914/CycleGAN-TF
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/itsuki8914/WGAN-GP-ResBlock-TensorFlow
https://github.com/itsuki8914/pix2pix-automaticColorization
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/itsuki8914/starGAN-LSGAN
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/itsuki8914/stylegan-TensorFlow
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/itsuki8914/wgan-gp-TensorFlow
https://github.com/ivankunyankin/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/ivanvovk/WaveGrad
https://github.com/ivclab/BigGAN-Generator-Pretrained-Pytorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/iver56/automatic-video-colorization
https://github.com/ivychill/insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/ivychill/reid
Joint Discriminative and Generative Learning for Person Re-identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.