https://github.com/seominseok0429/pytorch-warmup-cosine-lr
Bag of Tricks for Image Classification with Convolutional Neural Networks
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/septmars/DL
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/seqam-lab/GPDA
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
https://github.com/serfaniane/siamese_person_re_id
https://github.com/sergivalverde/nicMSlesions
https://github.com/serre-lab/gala_tpu
https://github.com/setharram/facenet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seujung/pytorch-vit
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/seunghwan1228/CNN_EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/seyrankhademi/ResNet_CIFAR10
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shahrukhx01/ocr-test
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/shallowlearn/sportsreid
https://github.com/shao-chi/ImageCaption
https://github.com/sherdencooper/tricks-in-deeplearning
Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/shijianjian/efficientnet-pytorch-3d
https://github.com/shinseung428/simGAN_NYU_Hand
Learning from Simulated and Unsupervised Images through Adversarial Training
https://github.com/shinya7y/UniverseNet
https://github.com/shishir-roy/decaNLP-with-BERT
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/shivammehta25/Matcha-TTS
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
https://github.com/shmulvad/zero-for-ner
https://github.com/shoaibahmed/CrossLayerPooling
https://github.com/shoji9x9/CIFAR-10-By-small-ResNet
https://github.com/shoji9x9/Fashion-MNIST-By-ResNet
https://github.com/shreyasc-13/off_domain_rlkit
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
https://github.com/shreyashankar/gpt3-sandbox
https://github.com/shtnkgm/VisionFrameworkSample
https://github.com/shunk031/chainer-MeanTeachers
https://github.com/sicara/easy-few-shot-learning
https://github.com/siddarth-c/FedGMA
https://github.com/siddharthbhonge/Neural_Style_transfer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sidify/resnet_focal_loss
https://github.com/sigeek/mldl-rgbd-da
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/sigeek/rgbd-da-project
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/sigtyp/ST2024
The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/simo-bat/Crack_detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.