Papers with Code 아카이브
Unsupervised Domain Adaptation
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Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fenghz/cosda
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
https://github.com/fengliu90/Butterfly
Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation
https://github.com/fengredrum/cnn-xla
https://github.com/flowritecom/flow-merge
https://github.com/francescodisalvo05/66DaysOfData
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
https://github.com/fremigereau/mtda_kd_reid
Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification
https://github.com/fungtion/DANN
https://github.com/fungtion/DSN
https://github.com/gabrieletiboni/Domain-adaptation-on-PACS-dataset
https://github.com/gatheluck/Stronghold
https://github.com/gchaperon/alexnet
https://github.com/gcr/torch-residual-networks
https://github.com/gentlezhu/shift-robust-gnns
https://github.com/ghif/drcn
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
https://github.com/glouppe/info8010-deep-learning
https://github.com/godwinrayanc/YOLOv1-Pytorch
https://github.com/google/flax/tree/master/examples/imagenet
https://github.com/gorilla-lab-scut/srdc-cvpr2020
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
https://github.com/gpleiss/limits_of_large_width
https://github.com/greatsharma/DeepLearning-Papers-Implementation
https://github.com/gundeep15/SCD_AI_Project
https://github.com/hanzhaoml/mdan
https://github.com/haonanguo/remote-sensing-chatgpt
https://github.com/happen2me/adda_pytorch
https://github.com/harsh-99/SCL
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/hbchen121/dgreid
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/hershd23/ObjectLocalizer
https://github.com/hitachi-rd-cv/MILA
MILA: Memory-Based Instance-Level Adaptation for Cross-Domain Object Detection
https://github.com/hongkunsun/paratranscnn
https://github.com/hongyi-zhang/Fixup
https://github.com/horse007666/ResNet
https://github.com/hsd1503/resnet1d
https://github.com/hsinyilin19/resnetvae
https://github.com/huitangtang/ViCatDA
https://github.com/hukefei/deep-person-reid-master
Learning Generalisable Omni-Scale Representations for Person Re-Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.