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/garibida/ReNoise-Inversion
https://github.com/gatheluck/Stronghold
https://github.com/gcervantes8/Game-Image-Generator
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/gcr/torch-residual-networks
https://github.com/gcwl/pytorch-cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/gcwl/pytorch-dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/gcwl/pytorch-pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/geekysethi/triplet-GAN
https://github.com/general-ken0bi/GAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/genforce/fairgen
Improving the Fairness of Deep Generative Models without Retraining
https://github.com/genforce/ghfeat
https://github.com/genforce/higan
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis
https://github.com/genforce/interfacegan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/genforce/sefa
https://github.com/gentlezhu/shift-robust-gnns
https://github.com/gerardoglz/GANs
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/gfoxx29/Image-Captioning
https://github.com/giannisdaras/multires_textual_inversion
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/gift-surg/fetal_brain_seg
https://github.com/ginn24/RefineDet-1
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/gitlimlab/ACGAN-PyTorch
https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmin7/pconv_implementation
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/gmum/nice_pytorch
https://github.com/godofpdog/MobileFaceNet_keras
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/godwinrayanc/YOLOv1-Pytorch
https://github.com/gohyojun15/ANT_diffusion
https://github.com/goldhuang/SRGAN-PyTorch
https://github.com/gongzix/neuroclips
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/google-deepmind/graphcast
DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models
https://github.com/google-research/sparf
Learning Accurate Dense Correspondences and When to Trust Them
https://github.com/google-research/trecs_image_generation
Text-to-Image Generation Grounded by Fine-Grained User Attention
https://github.com/google/dreambooth
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
https://github.com/google/flax/tree/master/examples/imagenet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.