https://github.com/NiteshBharadwaj/adaptsegnet-materials
Learning to Adapt Structured Output Space for Semantic Segmentation
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/NitishaS-812k/Monet-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/NiucunCode/Deep-Learning-proj2
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/Niwhskal/SRNet
https://github.com/NoahBarrett98/ProgressiveGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Novemser/deep-imitation
https://github.com/Noxxel/floter
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/Nyn-ynu/MCD
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/OFRIN/Tensorflow_Pixel2Pixel
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/OlivierLessard/Semantic-Image-Inpainting-with-Deep-Generative-Models
https://github.com/Olksndr/FaceShifter
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/Omkar-Ranadive/Domain-Adaptation-CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/One-sixth/CVAE-GAN_tensorlayer
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/Oorgien/Scene-Inpainting
Image Inpainting for Irregular Holes Using Partial Convolutions
https://github.com/OpenTalker/SadTalker
https://github.com/OsvaldN/APS360_Project
Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation
https://github.com/Our4514/CAGFUZZ
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/P4ppenheimer/NormalizingFlows
https://github.com/P4ppenheimer/NormalizingFlows_rnvp
https://github.com/PDEUXA/AIF_CYCLEGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/PKU-YuanGroup/UniWorld-V1
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
https://github.com/PPPrior/i3d-pytorch
https://github.com/PaYo90/Copy-of-ALAE
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter04
Image-to-Image Translation with Conditional Adversarial Networks
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter06
Semantic Image Synthesis with Spatially-Adaptive Normalization
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter07
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter08
https://github.com/PaddlePaddle/FleetX
ERNIE-ViLG: Unified Generative Pre-training for Bidirectional Vision-Language Generation
https://github.com/PaddlePaddle/FleetX/blob/develop/examples/resnet/train_fleet_sharding.py
ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
https://github.com/PaddlePaddle/PLSC
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/PaddlePaddle/PaddleClas
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/PaddlePaddle/PaddleDetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.