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/jm199504/VGG-NET
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jmaronas/calibration_MixupDNN_ARCLoss.pytorch
https://github.com/jmfacil/camconvs
CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
https://github.com/jmiemirza/actmad
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
https://github.com/jmnolte/hccnet
https://github.com/jo1jun/Vision_Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/joeynavarro/facial_expression_classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/johngear/eecs504
https://github.com/johnnylu305/resnet-50-101-152
https://github.com/jongcye/Domain.Adaptation.AcceleratedMR
Deep Learning with Domain Adaptation for Accelerated Projection-Reconstruction MR
https://github.com/jonmun/EPIC-KITCHENS-100_UDA_TA3N
https://github.com/jonmun/MM-SADA-code
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
https://github.com/jonnor/datascience-master
https://github.com/jooyae/NVIDIA_STYLEGAN
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jordan-bird/synthetic-fruit-image-generator
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/js-aguiar/wheat-object-detection
https://github.com/jtcass01/SIMON
https://github.com/jtiger958/pytorch-computer-vision-basic
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jtiger958/pytorch-computer-vision-tutorial
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/juergenlandauer/Maya-Challenge
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/juletx/lm-evaluation-harness
https://github.com/juliakreutzer/bandit-neuralmonkey
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning
https://github.com/julik43/IIMAS-USCS
https://github.com/jung-jun-uk/mixface
https://github.com/jungokasai/twist_decoding
Domain Adaptation of Neural Machine Translation by Lexicon Induction
https://github.com/junjiehu/dali
Domain Adaptation of Neural Machine Translation by Lexicon Induction
https://github.com/junkwhinger/fastautoaugment_jsh
https://github.com/junyongyou/triq
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/justinbellucci/cnn-visualizations-pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/jvanvugt/pytorch-domain-adaptation
https://github.com/jvoynow/ConvNet-Architectures
https://github.com/jwzhi/touchup-g
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
https://github.com/jxhuang0508/CVRN
Cross-View Regularization for Domain Adaptive Panoptic Segmentation
https://github.com/jxhuang0508/HCL
https://github.com/jxhuang0508/RDA
Uncertainty-Aware Unsupervised Domain Adaptation in Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.