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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/vietdhoang/resnet-18
https://github.com/viggin/domain-adaptation-toolbox
Learning Domain-Invariant Subspace using Domain Features and Independence Maximization
https://github.com/vill-lab/2024-aaai-hpt
https://github.com/vilm-ai/viet-llm-eval
https://github.com/vinay-EIP/EIP-session-5-assignment
https://github.com/vincent-vercruyssen/transfertools
https://github.com/vineeths96/Gradient-Compression
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vineeths96/heterogeneous-systems
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/vinod377/STN-OCR
https://github.com/vinod377/STN-OCR-Tensorflow
https://github.com/vinodkkurmi/DiscriminatorDomainAdaptation
Looking back at Labels: A Class based Domain Adaptation Technique
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer
https://github.com/vinuni-vishc/few-shot-transformer
https://github.com/vision4robotics/SiameseTracking4UAV
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
https://github.com/vita-epfl/rock-pytorch
https://github.com/vivienseguy/Large-Scale-OT
https://github.com/vkartik97/habitat-challenge-two-docker
https://github.com/vladthesav/MoldAI
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/vlievin/Unet
https://github.com/volcengine/vegiantmodel
https://github.com/vvekraman/Missing-Class-Robust-Domain-Adaptation-by-Unilateral-Alignment
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
https://github.com/vwegn/dm
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/walking-shadow/official_tssun
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
https://github.com/wang-ml-lab/multi-domain-active-learning
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
https://github.com/wang-ml-lab/unified-continual-learning
Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation
https://github.com/wang-pengfei/ucf
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/wangermeng2021/FastClassification
https://github.com/wangguanan/light-reid
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://github.com/wangkaihong/cdac
Exploring Consistency in Cross-Domain Transformer for Domain Adaptive Semantic Segmentation
https://github.com/wangsr126/mae-lite
https://github.com/wangyi111/international-archaeology-ai-challenge
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/wangyuan249/Mymmt767
https://github.com/wasilone11/ICPR-RIP-2024
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/watay147/tele_project
Generate To Adapt: Aligning Domains using Generative Adversarial Networks
https://github.com/waterdisappear/nudt4mstar
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.