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/iamilyasedunov/key_word_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ibm/techqa
https://github.com/icewing1996/baseline
https://github.com/idanachi/RegRec_and_PCM
Self-Supervised Learning for Domain Adaptation on Point-Clouds
https://github.com/iemprog/mimi
https://github.com/ievred/JCPOT
Optimal Transport for Multi-source Domain Adaptation under Target Shift
https://github.com/iiyama-lab/PS-VAEs
Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift
https://github.com/ildoonet/cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/ildoonet/pytorch-randaugment
RandAugment: Practical automated data augmentation with a reduced search space
https://github.com/ilyak93/GAIN-pytorch
https://github.com/imatif17/Joint-Unsupervised-Domain-adaption-and-Knowledge-distillation
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
https://github.com/imkoushik22/Walmart-Hackathon
https://github.com/innat/LearnedResizer-Vision-Transformer
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/innat/VideoMAE
https://github.com/insait-institute/lm-evaluation-harness-bg
https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset-Lukemia-Attri-MICCAI-2024
https://github.com/interestingzhuo/foodretreival
https://github.com/intsco/am-segmentation
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/ipieter/universal-distillation
https://github.com/ipl-uw/ZeDO-Release
Efficient Domain Adaptation via Generative Prior for 3D Infant Pose Estimation
https://github.com/iremeyiokur/multipie_ear_dataset
Domain Adaptation for Ear Recognition Using Deep Convolutional Neural Networks
https://github.com/irh-ca-team-car/attention-data
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
https://github.com/isaaccorley/hydro-foundation-model
https://github.com/isaachaw/GrabCarRecognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/isaacschaal/SG_training
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/isee-laboratory/pixelfade
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/isi-boston/ed-pooling
https://github.com/islamosmanubc/MedMAE
https://github.com/ithuanhuan/EANet-master
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
https://github.com/itijyou/ademxapp
https://github.com/iver56/audiomentations
https://github.com/ivychill/reid
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/j-nowak/covid-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/j3soon/arxiv-utils
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.