Papers with Code 아카이브
Domain Adaptation
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- 평가표
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- 보존된 평가 기록
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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/rafaelmata357/Flower-image-classifier
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/raferguson/CNN-Profile-Picture
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/raghakot/keras-resnet
https://github.com/raguilar-f/Thermal-Face-Recognition-Using-Convolutional-Neural-Networks-and-Transfer-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/raguilar-f/Thermal-face-recognition-using-transfer-learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rahul-t-p/ASVspoof-2019
https://github.com/rahul263-stack/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/rahulchaurasiya1/cautious-waddle
https://github.com/raj-gupta1/Flower-Species-Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/raj-gupta1/Tomato-leaves-disease-Predictor
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rajneeshaggarwal/google-efficientnet
https://github.com/ramanakshay/nanogpt
https://github.com/ravi02512/efficientdet-keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rayanramoul/Visual-Transformer-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/realmichaelye/Skin-Lesions-Classifier-ResNet50
https://github.com/redhat12345/Domain-Adaptation-Papers-and-Codes
https://github.com/reiinakano/invariant-risk-minimization
https://github.com/researchmm/SiamDW
https://github.com/resemble-ai/Resemblyzer
https://github.com/reyvaz/pneumothorax_detection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/reyvaz/steel-defect-segmentation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rf5/simple-speaker-embedding
https://github.com/rgeirhos/texture-vs-shape
https://github.com/rhero12/Slic
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/richieyoum/Pneumonia-diagnosis
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ricky40403/BlurPool
https://github.com/rickyHong/tfRecord-Caltech256-repl2
https://github.com/rickyhan/SimGAN-Captcha
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/rikichou/mmpose
https://github.com/rishabh-vij/Neural_Style_transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rishabmallick/Sign-language-live-predictor
https://github.com/rit-git/Snippext_public
https://github.com/rizqiamaliatuss/PotatoLeafDiseaseClassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/rizvansky/Internship-AI-Lab-IU-2020
On the limits of cross-domain generalization in automated X-ray prediction
https://github.com/rlagywns0213/cifar100_data_augmentation
RandAugment: Practical automated data augmentation with a reduced search space
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.