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/JoegameZhou/efficientnet-b0
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Jopapy19/CNN-App
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JuanCasian/ML-Speech-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Julien-pour/music_classifcation
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/JunMa11/COVID-19-CT-Seg-Benchmark
Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation
https://github.com/JunweiLiang/Object_Detection_Tracking
https://github.com/K-Mike/Automatic-salt-deposits-segmentation
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/Kadenze/siamese_net
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
https://github.com/KaimingHe/deep-residual-networks
https://github.com/Kalyan0821/NST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kalyan0821/myelinfoundry-challenge
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kano-Wu/Domain-Adversarial-Neural-Networks
https://github.com/KarT1994/EmnistRecognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarimaCha/CNN_Trust_Prediction
https://github.com/KatherLab/HIA
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs
https://github.com/Kaushal28/Bengali-AI
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/Kaushal28/CutMix-Regularization-using-PyTorch
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/KeiraZhao/MDAN
https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10
https://github.com/KellyHwong/rethinking_generalization
https://github.com/KentaItakura/CNN-classification-using-random-erasing-and-cut-out
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/Kevinz-code/CSRA
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/KiUngSong/Vision
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/KimSinjeong/SuperPoint_URP
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/Kin-Lau/ILGnet
https://github.com/King-Otaku/Emotion_Recognition_DNN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kolkir/feature-point-cnn
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/Kolkir/superpoint
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/KookHoiKim/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/Kyubyong/g2pC
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
https://github.com/LKLQQ/ssc_resnet50
https://github.com/LMaxence/Cifar10_Classification
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/LONGXUANX/CDFormer_code
Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
https://github.com/Lan1991Xu/ONE_NeurIPS2018
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.