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/moddy2024/resnet-9
https://github.com/monkey0head/Domain_Adaptation_thesis
https://github.com/mr-bulb/DL_basic_github
https://github.com/mrzzy/np-dl-assign-1
https://github.com/ms903-github/ACAL-pytorch
Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation
https://github.com/mszpc/osnet
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/mszpc/resnet18
https://github.com/musifahamran/FYP
https://github.com/mustafa1728/TA3N-Lightning
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation
https://github.com/mustafa1728/TA3N-Lightning-Kitchen_Domain_Adaptation
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation
https://github.com/ndb796/pytorch-adversarial-training-cifar
https://github.com/nicolalandro/Padam
https://github.com/northeastsquare/effficientnet
https://github.com/nsom/Basic-Models
https://github.com/nsom/VGG16
https://github.com/ntrin/CIFAR_10_ML
https://github.com/nv-tlabs/fDAL
https://github.com/olivesgatech/TA3N
Temporal Attentive Alignment for Large-Scale Video Domain Adaptation
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/onedayatatime0923/Cycle_Mcd_Gan
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/open-mmlab/OpenUnReID
Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmpose
https://github.com/openmatch/coco-dr
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
https://github.com/openvinotoolkit/deep-object-reid
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/orobix/mdd-domain-adaptation
https://github.com/oskyhn/CNNs-Without-Borders
https://github.com/osmr/imgclsmob
https://github.com/oxygen0605/ImageClassification
https://github.com/paperswithcode/paperswithcode-client
https://github.com/parviagrawal/IdenProf
https://github.com/patientzero/timage-icann2019
https://github.com/paulblum00/ResNet-Recreation
https://github.com/pawelbeza/ClothesDetectorModel
https://github.com/peteryuX/arcface-tf2
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.