https://github.com/freedomintelligence/huatuogpt-ii
공식 코드HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/fremigereau/mtda_kd_reid
공식 코드Knowledge Distillation for Multi-Target Domain Adaptation in Real-Time Person Re-Identification
https://github.com/freshman97/csu
공식 코드https://github.com/frezaeix/VISGA_Public
공식 코드https://github.com/frxxzhl/ssebt
공식 코드https://github.com/fszatkowski/cl-teacher-adaptation
공식 코드Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning
https://github.com/furqon3009/mdan
공식 코드Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions
https://github.com/g-pichler/knife
공식 코드A Differential Entropy Estimator for Training Neural Networks
https://github.com/gabriel-tjio/ash
공식 코드Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation
https://github.com/galaxy-zrx/edgehead
공식 코드https://github.com/gallenszl/MSMD-Net
공식 코드PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
https://github.com/gallenszl/pcwnet
공식 코드PCW-Net: Pyramid Combination and Warping Cost Volume for Stereo Matching
https://github.com/ganlumomo/thermal-uda-attention
공식 코드Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
https://github.com/ganperf/hcpr
공식 코드Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale
https://github.com/gaohan123/lam
공식 코드Consistency Regularization for Domain Generalization with Logit Attribution Matching
https://github.com/gaoyuan2/apcotta
공식 코드APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds
https://github.com/gaozhengqing/unient
공식 코드Unified Entropy Optimization for Open-Set Test-Time Adaptation
https://github.com/gaozhitong/multishiftseg
공식 코드Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts
https://github.com/gaussalgo/adaptor
공식 코드Adaptor: Objective-Centric Adaptation Framework for Language Models
https://github.com/gautierdag/tokenizer-bench
공식 코드Getting the most out of your tokenizer for pre-training and domain adaptation
https://github.com/gb-kgp/medvoc
공식 코드https://github.com/gdaosu/transferability-remote-sensing
공식 코드On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data
https://github.com/genentech/svae
공식 코드Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling
https://github.com/gengxuli/act
공식 코드Asymmetric Co-Training for Source-Free Few-Shot Domain Adaptation
https://github.com/genie-kim/pinthememory
공식 코드Pin the Memory: Learning to Generalize Semantic Segmentation
https://github.com/geometricbci/deep-optimal-transport-for-domain-adaptation-on-spd-manifolds
공식 코드Deep Optimal Transport for Domain Adaptation on SPD Manifolds
https://github.com/ghostish/open3dsot
공식 코드An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
https://github.com/ghrua/ngramres
공식 코드$N$-gram Is Back: Residual Learning of Neural Text Generation with $n$-gram Language Model
https://github.com/giaczara/dallv
공식 코드https://github.com/gianlucarloni/crocodile
공식 코드CROCODILE: Causality aids RObustness via COntrastive DIsentangled LEarning
https://github.com/ginawong/weighted_risk_invariance
공식 코드Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
https://github.com/gingasan/lemon
공식 코드Rethinking Masked Language Modeling for Chinese Spelling Correction
https://github.com/gingl/cmpa
공식 코드https://github.com/gist-ailab/domain-specific-block-selection-and-paired-view-pseudo-labeling-for-online-tta
공식 코드Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
https://github.com/git-ljc/umfc
공식 코드UMFC: Unsupervised Multi-Domain Feature Calibration for Vision-Language Models
https://github.com/giuliomattolin/confmix
공식 코드ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.