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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/majianthu/cda
공식 코드Causal Domain Adaptation with Copula Entropy based Conditional Independence Test
https://github.com/makeshn/lobef_byte_nmt
공식 코드https://github.com/malicd/sailor
공식 코드SAILOR: Scaling Anchors via Insights into Latent Object Representation
https://github.com/maloadba/mgenseg_2d
공식 코드https://github.com/mancinimassimiliano/CuMix
공식 코드https://github.com/mancinimassimiliano/adagraph
공식 코드Towards Recognizing New Semantic Concepts in New Visual Domains
https://github.com/mancinimassimiliano/caffe
공식 코드https://github.com/manogna-s/pSTarC
공식 코드pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation
https://github.com/manveertamber/enhancing_domain_adaptation
공식 코드Teaching Dense Retrieval Models to Specialize with Listwise Distillation and LLM Data Augmentation
https://github.com/maohaos2/MSFDA
공식 코드On Balancing Bias and Variance in Unsupervised Multi-Source-Free Domain Adaptation
https://github.com/marclafon/gallop
공식 코드GalLoP: Learning Global and Local Prompts for Vision-Language Models
https://github.com/marian-nmt/marian
공식 코드The Highs and Lows of Simple Lexical Domain Adaptation Approaches for Neural Machine Translation
https://github.com/mariodoebler/test-time-adaptation
공식 코드Introducing Intermediate Domains for Effective Self-Training during Test-Time
https://github.com/marrlab/domainlab
공식 코드https://github.com/marszhaoyt/sslchange
공식 코드SSLChange: A Self-supervised Change Detection Framework Based on Domain Adaptation
https://github.com/martinwimpff/eeg-continual
공식 코드https://github.com/martinwimpff/eeg-otta
공식 코드Calibration-free online test-time adaptation for electroencephalography motor imagery decoding
https://github.com/maryam089/sdvit
공식 코드https://github.com/masilab/da_ct_muscle_seg
공식 코드Single Slice Thigh CT Muscle Group Segmentation with Domain Adaptation and Self-Training
https://github.com/masseeh/dcaug
공식 코드https://github.com/mathilde-b/SFDA
공식 코드https://github.com/mathilde-b/SRDA
공식 코드https://github.com/mathilde-b/tta
공식 코드Test-Time Adaptation with Shape Moments for Image Segmentation
https://github.com/mattdl/DUA
공식 코드Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem
https://github.com/matthewbehrend/BNC
공식 코드https://github.com/matthewcym/gense
공식 코드Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework
https://github.com/mattialitrico/guiding-pseudo-labels-with-uncertainty-estimation-for-source-free-unsupervised-domain-adaptation
공식 코드Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
https://github.com/mattialitrico/guiding-pseudo-labels-with-uncertainty-estimation-for-test-time-adaptation
공식 코드Guiding Pseudo-labels with Uncertainty Estimation for Source-free Unsupervised Domain Adaptation
https://github.com/mattiasegu/darth
공식 코드DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
https://github.com/mawanqin2002/stacom2022ma
공식 코드https://github.com/maxiuw/uada3d
공식 코드https://github.com/maxwell1447/fairseq
공식 코드Towards Example-Based NMT with Multi-Levenshtein Transformers
https://github.com/maxwellyaoni/pace
공식 코드PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization
https://github.com/maxwellyin/mdaqa
공식 코드Source-Free Domain Adaptation for Question Answering with Masked Self-training
https://github.com/maxzanella/stata
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.