Papers with Code 아카이브
Image Generation
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- 평가표
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- 보존된 평가 기록
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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/avocardio/resnet_vs_convnext
https://github.com/awadalaa/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/awentzonline/image-analogies
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/awesomephant/vf-installation
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/awjuliani/Pix2Pix-Film
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/axium/Blind-Image-Deconvolution-using-Deep-Generative-Priors
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/axondeepseg/AxonDeepSynth
Unsupervised Medical Image Translation with Adversarial Diffusion Models
https://github.com/axrwl/axial-positional-embedding
https://github.com/ayanglab/hdl
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/aydao/stylegan2-surgery
https://github.com/ayiyoh/style-gan-pytorch
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/aymericdamien/TopDeepLearning
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
https://github.com/aysim/comingdowntoearth
Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization
https://github.com/ayulockin/deepimageinpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/ayushdabra/drone-images-semantic-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/lunar-landscape-images-segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushdabra/retinal-oct-images-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/gan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ayushgupta9198/stylegan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/azraelzhor/tf2-VAEAC
https://github.com/azzarelli/wape
https://github.com/b4nn3d/stylegan2
https://github.com/b4nn3d/stylegan2encoder
https://github.com/b8goal/PIRL-Residual-network
https://github.com/baaivision/emu3
https://github.com/baaivision/muse-pytorch
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/baaivision/vid2vid-zero
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/babyboy201/StyleGAN2
https://github.com/bacdavid/InfomaxVAE
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://github.com/bachnguyente/temporal-mgn
https://github.com/bailvwangzi/repulsion_loss_ssd
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/balakg/posewarp-cvpr2018
https://github.com/bamos/dcgan-completion.tensorflow
https://github.com/baofff/BiSM
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.