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/dvlab-research/VisionReasoner
Seg-Zero: Reasoning-Chain Guided Segmentation via Cognitive Reinforcement
https://github.com/dvlab-research/imbalanced-learning
https://github.com/dvlab-research/rescom
https://github.com/eaguaida/TF_EnsNet-
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/easeml/datascope
https://github.com/eddardd/gmm_msda
Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/elasticdeeplearning/edl
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/emla2805/vision-transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/enzomuschik/distilfnd
https://github.com/epic-kitchens/C1-Action-Recognition-TSN-TRN-TSM
https://github.com/epic-kitchens/epic-kitchens-slowfast
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/eric-erki/IdenProf
https://github.com/eric-erki/decaNLP
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/eric-yyjau/pytorch-superpoint
SuperPoint: Self-Supervised Interest Point Detection and Description
https://github.com/erichson/noisy_mixup
https://github.com/erictzeng/adda
https://github.com/eridgd/WCT-TF
https://github.com/eriklindernoren/Keras-GAN
https://github.com/eriklindernoren/PyTorch-GAN
https://github.com/erkil1452/gaze360
Gaze360: Physically Unconstrained Gaze Estimation in the Wild
https://github.com/erlendd/ddan
https://github.com/ermolenkodev/da-ssd
https://github.com/ermongroup/alignflow
AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows
https://github.com/erosenfeld/disagree_discrep
https://github.com/eshoyuan/taop2021-3rd-solution
https://github.com/estrellaxyu/differential-alignment-for-daod
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
https://github.com/etetteh/sota-data-augmentation-and-optimizers
Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/ethanjperez/true_few_shot
https://github.com/ewinata/CIFAR-10
https://github.com/explainingai-code/VIT-Pytorch
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/exploration-lab/iitk-semeval-2024-task-1
https://github.com/explosion/spacy-pkuseg
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.