https://github.com/lyonling/pix2pix
Unpaired Image-to-Image Translation using Cycle-Consistent 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/lyqcom/biggan
StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis
https://github.com/lyqcom/models-master
https://github.com/lyronctk/quant-noisier-speech
Training with Quantization Noise for Extreme Model Compression
https://github.com/lywang12/cupi-domain
https://github.com/lywang12/cuti-domain
https://github.com/lzhbrian/alias-free-gan-explanation
https://github.com/lzhengchun/deep-image-prior-tensorflow
https://github.com/lzhmeng/lca
https://github.com/lzw-lzw/awesome-remote-sensing-vision-language-models
Vision-Language Models in Remote Sensing: Current Progress and Future Trends
https://github.com/lzx6/pytorch_DA
https://github.com/mabhay3420/Deep-Into-CNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/macderru/hyperdomainnet
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
https://github.com/maciejdomagala/DLWP_notes
https://github.com/mackitron/COVID19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/magnificent1208/r3det-on-mmdetection
https://github.com/mahbouldom/StyleGAN2
https://github.com/mahdip72/InstructPix2Pix
InstructPix2Pix: Learning to Follow Image Editing Instructions
https://github.com/mahfuzmohammad/fixed-point-gan
https://github.com/mahkons/flows
https://github.com/mahmoodlab/hest
https://github.com/mahotani/ADVERSARIAL-AUDIO-SYNTHESIS
https://github.com/makatx/YOLO_ResNet
https://github.com/malbergo/stochastic-interpolants
https://github.com/malin9402/2020-0221
https://github.com/malin9402/retiface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/maminio/sample-generating
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/manankshastri/ART
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mandalarotation/PixdosepiX-OpenKBP---2020-AAPM-Grand-Challenge-
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/mandubian/pytorch-neural-ode
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
https://github.com/manicman1999/CycleGAN256-BR
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/manicman1999/StyleGAN-Keras
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/manicman1999/StyleGAN-Tensorflow-2.0
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/manicman1999/StyleGAN2-Tensorflow-2.0
https://github.com/manicman1999/Sword-GAN32
https://github.com/manicman1999/precision-recall-keras
Improved Precision and Recall Metric for Assessing Generative Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.