https://github.com/PruneTruong/GLU-Net
Learning Accurate Dense Correspondences and When to Trust Them
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/PsorTheDoctor/microarray-data
https://github.com/PuchatekwSzortach/pix2pix
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Puzer/stylegan-encoder
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/QXL4515/CAGFuzz
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Qi-Xian/color-transfer_HW1
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Qiyuan-Ge/PaintMind
Muse: Text-To-Image Generation via Masked Generative Transformers
https://github.com/QuangBK/cStyleGAN
A Style-Based Generator Architecture for Generative Adversarial Networks
https://github.com/QuickSolverKyle/Tensorflow-MyGANs
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/RAF96/ifmo-2019-deep-learning-coursework
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
https://github.com/RAGHAV2998/Everybody-Can-Dance-Now-Video-game-version-
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/RRoundTable/OceanLitter_dataset_generator
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Rajjeshwar/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Rakshith-Manandi/text-to-image-using-GAN
https://github.com/Ram81/AC-VAEGAN-PyTorch
https://github.com/RanBezen/cycleGan_handwriting
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ranchofromxgd/DCGAN_mnist
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Raykoooo/IAST
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Recognito-Vision/Face-SDK-Linux-Demos
Cross-Age Contrastive Learning for Age-Invariant Face Recognition
https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
https://github.com/ReubenDo/U-HVED
https://github.com/RezisEwig/cyclegan
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Rick-McCoy/Reformer-pytorch
https://github.com/RikoLi/gan-acgface
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Ritam9/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Ritam9/DualGAN
DualGAN: Unsupervised Dual Learning for Image-to-Image Translation
https://github.com/Rivendile/ACL-GAN
Unpaired Image-to-Image Translation using Adversarial Consistency Loss
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/RocketFlash/easy_metric_learning/tree/master/tools
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Rohed/ml-1
https://github.com/Rongpeng-Lin/StarGAN-Multi-domain-use-tensorflow
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
https://github.com/RudreshVeerkhare/StyleGan
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/RuiLiFeng/LAE
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.