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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/s-mohammad-hashemi/pppl
공식 코드General Domain Adaptation Through Proportional Progressive Pseudo Labeling
https://github.com/s4nyam/marioec
공식 코드https://github.com/sadangelf/fedrf-tca
공식 코드Robust and Communication-Efficient Federated Domain Adaptation via Random Features
https://github.com/sadman-jahan/aid-ucm-degradingweather
공식 코드Continual Domain Adaptation on Aerial Images under Gradually Degrading Weather
https://github.com/sai990323/tallrec
공식 코드https://github.com/sail-sg/metaformer
공식 코드https://github.com/sail-sg/volo
공식 코드https://github.com/saili0103/fairm
공식 코드https://github.com/saili0103/tensordg
공식 코드Multi-dimensional domain generalization with low-rank structures
https://github.com/sakaridis/MGCDA
공식 코드https://github.com/sakurajimamaiii/tsd
공식 코드https://github.com/salehsargolzaee/hopfield-integrated-test
공식 코드Improving Out-of-Distribution Data Handling and Corruption Resistance via Modern Hopfield Networks
https://github.com/salesforce/augtriever
공식 코드AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
https://github.com/salesforce/decaNLP
공식 코드The Natural Language Decathlon: Multitask Learning as Question Answering
https://github.com/salesforce/ensemble-of-averages
공식 코드Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization
https://github.com/salesforceairesearch/findap
공식 코드Demystifying Domain-adaptive Post-training for Financial LLMs
https://github.com/salmank255/ROAD_Waymo_Baseline
공식 코드ROAD-Waymo: Action Awareness at Scale for Autonomous Driving
https://github.com/saltoricristiano/SF-UDA-3DV
공식 코드SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection
https://github.com/saltoricristiano/cosmix-uda
공식 코드CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
https://github.com/saltoricristiano/gipso-sfouda
공식 코드GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
https://github.com/saltoricristiano/lidog
공식 코드Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
https://github.com/samar-khanna/cropmap
공식 코드Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
https://github.com/samia067/shoerinsics
공식 코드Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos
https://github.com/samiraabnar/Gift
공식 코드Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
https://github.com/samsunglabs/sympie
공식 코드https://github.com/samuki/reinforce-joey
공식 코드Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation
https://github.com/sanoojan/clip-drdg
공식 코드https://github.com/sanphouwang/fedccrl
공식 코드FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
https://github.com/saptarshi059/cdqa-v1-targetted-pretraining
공식 코드TOP-Training: Target-Oriented Pretraining for Medical Extractive Question Answering
https://github.com/saptarshi059/generalization-hypothesis
공식 코드Exploring Language Model Generalization in Low-Resource Extractive QA
https://github.com/saqibjaved1/QT-DoG
공식 코드QT-DoG: Quantization-aware Training for Domain Generalization
https://github.com/sarihust/sammed
공식 코드Leveraging SAM for Single-Source Domain Generalization in Medical Image Segmentation
https://github.com/sarmadfismael/lrm_i2i
공식 코드https://github.com/sarthaxxxxx/av-c-robustness-benchmark
공식 코드https://github.com/sarthaxxxxx/palm
공식 코드PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.