https://github.com/youwenjing/reid_mgn-dgnet
Joint Discriminative and Generative Learning for Person Re-identification
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/yuan-zm/dgt-st
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yw0nam/DenseNet
https://github.com/zhangxinyu-xyz/PAST-ReID
Self-training with progressive augmentation for unsupervised cross-domain person re-identification
https://github.com/zheangh/dfg
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
https://github.com/zhjscut/IterLNL
https://github.com/zjZSTU/ResNet
https://github.com/zkcys001/UDAStrongBaseline
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/zwenyu/UniSSDA
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/zysymu/AdaMatch-pytorch
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://gitlab.com/birder/birder
https://worksheets.codalab.org/worksheets/0xfafccca55b584e6eb1cf71979ad8e778
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.