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/andrecianflone/vector_quantization
https://github.com/andrewjong/Global-Flow-Local-Attention-VTryon
Deep Image Spatial Transformation for Person Image Generation
https://github.com/andrewlstewart/StereoNet_PyTorch
https://github.com/andrewowens/multisensory
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/andrewpku/DCGAN_project
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/andrijdavid/clinical-brain-interface-2020
https://github.com/angelvillar96/FaceEmoji
https://github.com/anglihust/Unsupervised-vessel-segmentation
https://github.com/anguyen8/sam
https://github.com/anguyen8/shape-bias
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
https://github.com/anh-nn01/Satellite-Imagery-to-Map-Translation-using-Pix2Pix-GAN-framework
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/anhtu293/Vietnamese-Celebrity-Face-Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/anibali/dsnt-pose2d
https://github.com/aniket-patra-1998/FLOWER_RECOGNITION
https://github.com/aniketpokale10/RenderForCNN_modified
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views
https://github.com/anirbrhm/ImageCaptioning
https://github.com/aniruddha343/Two-stream-Encoder-Decoder-Network-for-Image-Forensics
Two-stream Encoder-Decoder Network for Localizing Image Forgeries
https://github.com/anitakumarijena/Image_Captioning
https://github.com/anjalipemmaraju/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/anjandeepsahni/face_classification
https://github.com/anjandeepsahni/face_recognition
https://github.com/ankit-vaghela30/Google-landmark-prediction
https://github.com/ankitAMD/Self-Attention-GAN-master_pytorch
https://github.com/annegnx/PnP-Flow
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
https://github.com/anonymous0x233/ReuseAndDiffuse
Reuse and Diffuse: Iterative Denoising for Text-to-Video Generation
https://github.com/anordertoreclaim/PixelCNN
https://github.com/anoushkrit/Knowledge
https://github.com/ansrlm/ML_DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/antoinedemathelin/wann
https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/anuraggoel70/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/anushka-s/Image-restoration-using-deep-image-prior
https://github.com/anushkaw055/Image-Inpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/aobaruwa/TensorFlow-Projects
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/apisarek/gan_advances
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.