Papers with Code 아카이브
Image Generation
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- 평가표
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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/bingxinhu/arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/bioinfomachinelearning/gcpnet
Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/bionanoimaging/cellSTORM-Tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/birdortyedi/vcnet-blind-image-inpainting
https://github.com/bispl-kaist/tf_CollaGAN_BRATS
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GAN
https://github.com/bit-ml/dolos
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/bitcjm/GPEN_REPO
GAN Prior Embedded Network for Blind Face Restoration in the Wild
https://github.com/biweidai/SINF
https://github.com/bkkaggle/jax-dcgan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/blablabananarama/ukiyoGAN
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
https://github.com/black0017/MedicalZooPytorch
https://github.com/blandocs/improved-precision-and-recall-metric-pytorch
Improved Precision and Recall Metric for Assessing Generative Models
https://github.com/bloc97/CrossAttentionControl
https://github.com/blufzzz/Design-Inspiration-from-Generative-Networks
https://github.com/blzzrd/ComputerVisionFinal
https://github.com/bmy4415/DMLAB-intern
https://github.com/bobqywei/inpainting-partial-conv
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/boge8888/Watermark-removal-GUI
https://github.com/bogedy/intro_dfc
IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
https://github.com/boris127/vehicle-detection
https://github.com/borisdayma/dalle-mini
https://github.com/bornfly-detachment/asymmetric_magvitv2
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
https://github.com/bot66/mnistdiffusion
https://github.com/bourcierj/rdfia-tme10-gan
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/bprabhakar/text-to-image
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
https://github.com/brade31919/SRGAN-tensorflow
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/brandontrabucco/da-fusion
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
https://github.com/brenoskuk/recvis
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/brightyoun/Video-Style-Transfer
Instance Normalization: The Missing Ingredient for Fast Stylization
https://github.com/bruno-sartori/imageGenerator
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/bryandlee/FreezeG
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/brycexu/MR-Residual-Net
https://github.com/bthorne93/DustVAEder
https://github.com/btxuyenHCMUS/generative
https://github.com/btxuyenHCMUS/inpainting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.