https://github.com/LeDuySon/torchreid_uet_lab
Learning Generalisable Omni-Scale Representations for Person Re-Identification
Papers with Code 아카이브
Domain Adaptation
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Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.
Further readings:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Legoons/Melanoma_classification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/LeiLiu-byte/the-Essence-of-LT-Rocognition
https://github.com/Leinadh/avatar-image-generator
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
https://github.com/Lester1711/DSI15_Capstone
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiYingwei/ShapeTextureDebiasedTraining
https://github.com/LiYinqi/DIVE
https://github.com/LichenXia198/Deeping-Learning-project
Multi-source Domain Adaptation via Weighted Joint Distributions Optimal Transport
https://github.com/Lieberk/Paddle-FSL-Baseline
https://github.com/Lily-Le/EcoTTA
EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization
https://github.com/Little-Frog-233/imagecluster
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/LiwenWang919/M3-UDA
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
https://github.com/Lornatang/CoGAN-PyTorch
https://github.com/Lornatang/ResNet-PyTorch
https://github.com/Lornatang/tensorflow-vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Luffy03/DCA
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/LuigiRussoDev/Covid19Detection
https://github.com/LuigiRussoDev/ResNets
https://github.com/LukasBommes/PV-Mapper
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning
https://github.com/MKYucel/zero_shot_corruption_benchmarks
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
https://github.com/ML-Bee/associative-alignment-fs
https://github.com/MLOPTPSU/FedTorch
https://github.com/MLOPTPSU/TorchFed
https://github.com/MNaseerSubhani/LSE
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation
https://github.com/MRLoghmani/relative-rotation
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet
https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnext
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/MS-Mind/MS-Code-06/tree/main/Neighbor2Neighbor
Generalization through Memorization: Nearest Neighbor Language Models
https://github.com/MS-Mind/MS-Code-06/tree/main/osnet
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/MagicLeapResearch/SuperPointPretrainedNetwork
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/MaliParag/ScanSSD
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Mandar-Sharma/TCube
https://github.com/MarkHershey/arxiv-dl
https://github.com/MarvinMartin24/MADA-PL
https://github.com/Masao-Taketani/FOTS_OCR
https://github.com/MathiasStensrud/autoBleep
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.