Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/nttcslab/dcase2023_task2_evaluator
https://github.com/nuoaier/BaiduStar2020-18-Competition-Solution
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/nv-tlabs/fDAL
https://github.com/nyu-mll/multiNLI
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
https://github.com/oddskool/invariant_risk_minimization_reproduced
https://github.com/oellop/facial_expression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ofa-sys/chinese-clip
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/olivesgatech/TA3N
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/om00839/machine-suneung
Multi-Task Deep Neural Networks for Natural Language Understanding
https://github.com/omercohen93/superpoint-pytorch
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/onedayatatime0923/Cycle_Mcd_Gan
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/oneflow-inc/libai
https://github.com/open-edge-platform/geti
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/open-edge-platform/training_extensions
Aggregated Residual Transformations for Deep Neural Networks
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
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmdetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/open-mmlab/mmpose
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmpretrain
https://github.com/open-mmlab/mmselfsup
https://github.com/openbiolink/promptsource
https://github.com/opengptx/lm-evaluation-harness
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/orparask/VS-Loss
https://github.com/oskyhn/CNNs-Without-Borders
https://github.com/osmr/imgclsmob
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ostadabbas/infant-postural-symmetry
Invariant Representation Learning for Infant Pose Estimation with Small Data
https://github.com/owennju/fgrada
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
https://github.com/oxygen0605/ImageClassification
https://github.com/paddlepaddle/edl
Bag of Tricks for Image Classification with Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.