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/simongrest/farm-pin-crop-detection-challenge
https://github.com/simongrest/kaggle-freesound-audio-tagging-2019
https://github.com/singh-shakti94/Deep-Learning-Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sitegui/ceci-nest-pas-un-chat
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sithu31296/semantic-segmentation
https://github.com/skchen1993/TrangFG
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/skhetarpal/Prostate_Cancer_Grade_Assessment
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/skkuse02/2019fall_41class_team8
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/slayellow/CalibNet_with_Pytorch
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
https://github.com/sliao-mi-luku/Galaxy-Zoo-Classification
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/smarton-empower/smarton-ai
https://github.com/smc-x/ms-resnetv2
https://github.com/smhall97/deep_dreaming_music
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/smile-data/smile
https://github.com/smilelab-fl/fednoisy
https://github.com/smilingwolf/sw-cv-modelzoo
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/smitheric95/MoCoViT-PyTorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/smj007/Superpoint_Pytorch
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/smkim7-kr/AdaMatch-pytorch
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/smu-ivpl/DeepfakeDetection
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/sneakatyou/ViT-Tensorflow-2.0
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/sobalgi/cuda
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
https://github.com/sohyun-l/ExLPose
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
https://github.com/sonnguyen129/deep-feature-rotation
https://github.com/soumik12345/Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/soumith/convnet-benchmarks
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sour4bh/cifar-10
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sovit-123/Deep-Learning-Image-Captioning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/soyoung97/fairseq-mlm-scoring
https://github.com/soyoung97/mlm-scoring
https://github.com/sppsps/VGG-16-classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/spursgozmy/tabledreamer
https://github.com/sridharmahadevan/Geodesic-Covariance-Alignment
A Unified Framework for Domain Adaptation using Metric Learning on Manifolds
https://github.com/sroutray/da-ganin
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.