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/chunhuililili/mt_dnn
Posterior Differential Regularization with f-divergence for Improving Model Robustness
https://github.com/chunyangx/IRM_research
https://github.com/cinastanbean/Pytorch-Multi-Task-Multi-class-Classification
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/cj-mclaughlin/segmentation_research
https://github.com/cjf8899/Pytorch_ResNeXt
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cjshui/wadn
https://github.com/ckyrkou/EmergencyNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/claudiashi57/nice
https://github.com/climsocana/tecb-de
https://github.com/clovaai/rexnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/clvrai/representation-learning-by-learning-to-count
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cocopambag/ResNet
https://github.com/code-implementation1/Code1/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/code-implementation1/Code2/tree/main/Alexnet
https://github.com/code-implementation1/Code3/tree/main/FaceAttribute
https://github.com/code-implementation1/Code3/tree/main/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/code-implementation1/Code4/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/code-implementation1/Code5/tree/main/lresnet100e_ir
https://github.com/code-implementation1/Code7/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/code-implementation1/Code7/tree/main/resnetv2
https://github.com/code-implementation1/Code9/tree/main/resnest
https://github.com/code-implementation1/Code9/tree/main/vgg
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/codedotal/gpt-code-clippy
https://github.com/codegoat24/unifiedreward
Delving into RL for Image Generation with CoT: A Study on DPO vs. GRPO
https://github.com/codelion/adaptive-classifier
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
https://github.com/coling2022-depro/depro
https://github.com/computerhistory/AlexNet-Source-Code
https://github.com/conceptofmind/ViT-haiku
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/constantinosskitsas/Framework_GraphAlignment
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
https://github.com/contextlab/abstract2paper
https://github.com/copenlu/TESTED
https://github.com/coqui-ai/TTS
https://github.com/coradlut/bi-jros
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
https://github.com/cordercorder/knn-models
Generalization through Memorization: Nearest Neighbor Language Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.