Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/lucidrains/phenaki-pytorch
https://github.com/lucidrains/pi-GAN-pytorch
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
https://github.com/lucidrains/recurrent-interface-network-pytorch
https://github.com/lucidrains/reformer-pytorch
https://github.com/lucidrains/routing-transformer
Efficient Content-Based Sparse Attention with Routing Transformers
https://github.com/lucidrains/sinkhorn-transformer
https://github.com/lucidrains/siren-pytorch
Implicit Neural Representations with Periodic Activation Functions
https://github.com/lucidrains/soft-moe-pytorch
RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths
https://github.com/lucidrains/soundstorm-pytorch
https://github.com/lucidrains/stylegan2-pytorch
https://github.com/lucidrains/transganformer
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/lucidrains/vector-quantize-pytorch
https://github.com/lucidrains/voicebox-pytorch
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent Spaces
https://github.com/luckycallor/insightface-tensorflow
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/luckyfarnon/StyleGAN22
https://github.com/luckystarufo/SIREN_out_of_sample_tests
Implicit Neural Representations with Periodic Activation Functions
https://github.com/lucylow/salty-wet-man
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lukasuz/Banding-Pattern-Extraction
https://github.com/luke-grassroot/scratchgan
https://github.com/lukeandshuo/IR2VI_journal
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/lukedeo/keras-acgan
https://github.com/lukemelas/realfusion
ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation
https://github.com/lukemelas/unsupervised-image-segmentation
https://github.com/lukovnikov/improved_wgan_training
https://github.com/lunz-s/DeepAdverserialRegulariser
https://github.com/lupalab/flowscan
https://github.com/lupalab/posterior-matching
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images
https://github.com/luqui/big-sleep
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/lvyufeng/MindSpore-GAN
https://github.com/lvyufeng/denoising-diffusion-mindspore
https://github.com/lx709/vrsbench
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/lxy000719/ResNet-50-pneumonia-classification
https://github.com/ly015/intrinsic_flow
https://github.com/lyk19940625/MyRFBNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/lym29/DASeg
Learning to Adapt Structured Output Space for Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.