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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/SunnerLi/Cross-you-in-style
Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts
https://github.com/SupratimH/neural-style-transfer
https://github.com/SwordHolderSH/neural-style-pytorch
https://github.com/SyBorg91/pose-estimation-detection
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/SyedUmaidAhmed/Pose-Detection-Sitting-Standing
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
https://github.com/SystemErrorWang/Joint-Bilateral-Photorealistic-Style-Transfer
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
https://github.com/T-C-J/GAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/T-C-J/shuiying
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/TAMU-VITA/AGD
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
https://github.com/TAMU-VITA/DeblurGANv2
DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better
https://github.com/TArdelean/DynamicConvolution
https://github.com/TFNTF/PostEdit
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference
https://github.com/TJJTJJTJJ/pytorch_CycleGAN_frame
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/TOPO-EPFL/CrossLoc-Benchmark-Datasets
CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data
https://github.com/TahmasbiM/Example
https://github.com/Talk2Levi/DJL
MelNet: A Generative Model for Audio in the Frequency Domain
https://github.com/TanakitInt/SRCNN-anime
https://github.com/TanguyJeanneau/white-mirror
https://github.com/Taoudi/Monet-ization-of-Images
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/TariqBerrada/Pose-Estimation-with-Stacked-Hourglass
https://github.com/TeCSAR-UNCC/EfficientHRNet
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
https://github.com/Tee0125/pytorch-fast-style-transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/TejasBajania/Mtech_pro
https://github.com/TejasBajania/Mtech_thesis_project
https://github.com/Tencent/tencent-ml-images
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
https://github.com/ThanhDinhDat/center_net_points
https://github.com/TheConfused1/Real-time-neural-style-transfer
https://github.com/TheInfamousWayne/CheXNet
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
https://github.com/TheStageAI/TorchIntegral
https://github.com/Thehunk1206/Image-Restorers
https://github.com/Theopetitjean/DenseFusion_R_Invariant
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
https://github.com/ThibaultGROUEIX/3D-CODED
https://github.com/Thinklab-SJTU/PCA-GM
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/Thinklab-SJTU/ThinkMatch
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/ThomasDupiereux/hpgan
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.