https://github.com/djwhsdj/vw-sdk
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/dkn16/few-shot-gan-adaptation
https://github.com/dkn16/stylegan2-pytorch
https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/SinGanMindspore
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/dmdomo/AttentionGAN
Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation
https://github.com/dmizr/phuber
https://github.com/dmonterom/face_recognition_TF2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/dnddnjs/pytorch-cifar10
https://github.com/dnwjddl/Inpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/doc-doc/NExT-OE
https://github.com/doiodl/pixelcnn-rnn
https://github.com/domainadaptation/salad
https://github.com/dome272/MaskGIT-pytorch
https://github.com/dome272/ProjectedGAN-pytorch
https://github.com/dome272/VQGAN
https://github.com/dome272/vqgan-pytorch
https://github.com/donand/GAN_pytorch
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/donydchen/ganimation_replicate
GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/dopplerchase/cira-diff
https://github.com/dotwang/hknas
DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification
https://github.com/dougsouza/efficient-t2i
https://github.com/dqiaole/zits
Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding
https://github.com/dreamgonfly/GAN-tutorial
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/drexelai/protein-nets
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/drexubery/viewcrafter
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
https://github.com/dsl-lab/fvmd-frechet-video-motion-distance
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
https://github.com/dsorge3/eeg_visual_classification_project
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
https://github.com/dssbxx/k_labels_resnet50
https://github.com/dstein64/pastiche
https://github.com/dterjek/adversarial_lipschitz_regularization
https://github.com/ducbvh/Facial-De-occlusion-Network
Facial De-occlusion Network for Virtual Telepresence Systems
https://github.com/duchaba/Norwegian_Blue_Parrot_k2fa_AI
https://github.com/ducminhkhoi/Amodal-Instance-Seg-ASBU
https://github.com/duskvirkus/alias-free-gan
https://github.com/duskvirkus/alias-free-gan-pytorch-lightning
https://github.com/duskvirkus/stylegan2-ada-lightning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.