https://github.com/Shumway82/CycleGAN
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Papers with Code 아카이브
2D Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Shumway82/U-Net-Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/ShunyuYao/blitznet_instance_segment
https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/SiavashCS/sgan_simple
https://github.com/SiddhanthHegde/Custom-Neural-Style-Transfer
https://github.com/Sidu28/Voxel_att
Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection
https://github.com/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SilasHenderson/AI
https://github.com/SilasHenderson/ai_notes
https://github.com/SilvioGiancola/TrackingNet-devkit
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
https://github.com/SilvioGiancola/maskrcnn-benchmark
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
https://github.com/Sim-ai/mmdetection2
https://github.com/SimeonKraev/Musical-style-transfer-with-cycle-consistent-generative-adversarial-neural-networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/SimonThomine/DistillationAD
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/SimonThomine/MixedTeacher
Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization
https://github.com/SimonThomine/RememberingNormality
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/Simurgh818/UCSF_QBI_Hackathon_ImageSegmentor
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Singh-sid930/YOLO_pytorch
https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/SiriusKY/SceneTextDetector
https://github.com/SixQuant/U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/SkalskiP/make-sense
https://github.com/SkiddieAhn/Code-WinCLIP
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
https://github.com/Skywalker666666/DynaSLAM_4DVD
DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes
https://github.com/SlongLiu/ASL_reproduce
https://github.com/SmallMunich/nutonomy_pointpillars
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/Smallflyfly/yolov3_ultralytics
https://github.com/Smiler-Jin/Yolov4
https://github.com/Smiler-Jin/frustum_pointnet
https://github.com/SnailDev/github-hot-hub
You Only Learn One Representation: Unified Network for Multiple Tasks
https://github.com/Snarci/YOLO-Para
https://github.com/Sobeit-Tim/NuguEyeTest
https://github.com/SocieteGenevoiseDonnees/FiltersStylesDomains
https://github.com/Somoina/OpenHorse
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.