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/hanehein921/StyleGAN2
https://github.com/hangligit/InterpretDiffusion
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation
https://github.com/hanq0212/IIAE-Pytorch
Variational Interaction Information Maximization for Cross-domain Disentanglement
https://github.com/hanyoseob/pytorch-sngan
https://github.com/hanzhanggit/StackGAN-Pytorch
https://github.com/hanzhanggit/StackGAN-inception-model
https://github.com/hanzhaoml/mdan
https://github.com/hao-qiang/GANimation-tf
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/haonanguo/remote-sensing-chatgpt
https://github.com/haozheliu-st/t-gate
https://github.com/happen2me/adda_pytorch
https://github.com/happy-jihye/Cartoon-StyleGAN
https://github.com/happynear/AMSoftmax
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/haratzouvara/image_restoration
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/hardbyte/sorting-gym
https://github.com/hariv/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/harpreetsodhi/ChangeMyPet-Deep-Learning-Model
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/harpreetsodhi/ChangeMyPet_Deep_Learning_Model
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/harsh-99/SCL
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/harshalnishar/dcgan_cifar10
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/harshitbhushan68/Pix-2-Pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/harshm121/m3l
https://github.com/haru-256/pix2pixHD.pytorch
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/harvard-visionlab/lrm-steering
Hierarchical Text-Conditional Image Generation with CLIP Latents
https://github.com/hashi0203/image-captioning
https://github.com/hawkit/edge-tst
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/hawkiyc/WaveGAN_for_12_Leads_ECG_Signals
https://github.com/hazdzz/BGU
https://github.com/hcholab/k-salsa
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/hcygeorge/aoi_defect_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/healthcare-robotics/mr-gan
https://github.com/heavenstobetsy/ArtGenerationwithStyleGan
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/hectic97/DCGAN-pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/hehaodele/ProbGAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.