Papers with Code 아카이브
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/CorneliusHsiao/FoodMethodGAN
https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork
https://github.com/CryptoSalamander/pytorch_paper_implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/CtrlZ1/Domain-Adaptation-Algorithms
https://github.com/CtrlZ1/Domain-Adaptation-Algorithms/tree/main/pytorch-DeepJDOT
DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation
https://github.com/CtrlZ1/Domain-Adaptive-CodeBase
https://github.com/CtrlZ1/transferLearningAlgorithms/tree/main/pytorch-JCPOT
Optimal Transport for Multi-source Domain Adaptation under Target Shift
https://github.com/Cyprien0105/DataScience
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/D-X-Y/ResNeXt-DenseNet
https://github.com/DCSaunders/tagged-gender-coref
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
https://github.com/DMarwan/NutricIA
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/DaikiTanak/manifold_mixup
https://github.com/Daividao/semantic-segmentation-deeplearning
When Unseen Domain Generalization is Unnecessary? Rethinking Data Augmentation
https://github.com/DarshanDeshpande/jax-models
https://github.com/Darth-Kronos/Unsupervised-Domain-Adaptation
https://github.com/DavidLandup0/deepvision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/DeLightCMU/RSC
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DeepBrainsMe/FSnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/DeepMark/deepmark
https://github.com/DeepVoltaire/AutoAugment
https://github.com/DelTA-Lab-IITK/CADA
https://github.com/DelTA-Lab-IITK/CD3A
Curriculum based Dropout Discriminator for Domain Adaptation
https://github.com/Demonhesusheng/Reid
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/DengpanFu/LUPerson
Self-Supervised Pre-Training for Transformer-Based Person Re-Identification
https://github.com/DevBruce/CutMixImageDataGenerator_For_Keras
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/DiegoArcelli/De-Stylization-Network
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/Diegomangasco/Vision-Language-AML
Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation
https://github.com/Dmitrsl/Tools
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/DominikBatic/EndoViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Duplums/bhb10k-dl-benchmark
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/E-DEEP/PapersReview
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.