https://github.com/pz-white/drugban
공식 코드Interpretable bilinear attention network with domain adaptation improves drug-target prediction
Papers with Code 아카이브
Domain Adaptation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/qbetterk/DAML
공식 코드https://github.com/qdata/pgrad
공식 코드PGrad: Learning Principal Gradients For Domain Generalization
https://github.com/qgking/DASC_COVID19
공식 코드Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
https://github.com/qi-yangsjtu/it-pcqa
공식 코드No-Reference Point Cloud Quality Assessment via Domain Adaptation
https://github.com/qi5lei/dsaf
공식 코드https://github.com/qianyuzqy/CAMix
공식 코드Context-Aware Mixup for Domain Adaptive Semantic Segmentation
https://github.com/qianyuzqy/RCCR
공식 코드Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
https://github.com/qianyuzqy/iadg
공식 코드https://github.com/qiaoruiyt/noiserobustdg
공식 코드Understanding Domain Generalization: A Noise Robustness Perspective
https://github.com/qihoo360/light-r1
공식 코드Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyond
https://github.com/qinenergy/adanet
공식 코드Semi-Supervised Learning by Augmented Distribution Alignment
https://github.com/qinenergy/corda
공식 코드Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
https://github.com/qinenergy/cotta
공식 코드https://github.com/qinenergy/syn2real
공식 코드Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
https://github.com/qingjie99/MetFA
공식 코드Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment
https://github.com/qinhongda8/ldds
공식 코드Language-Driven Dual Style Mixing for Single-Domain Generalized Object Detection
https://github.com/qintianjian-lab/ras4dg
공식 코드https://github.com/qinzheng2000/generaltrack
공식 코드https://github.com/qiumuyang/siab
공식 코드https://github.com/qiurui4shu/PIG
공식 코드https://github.com/qiuzh20/emoe
공식 코드https://github.com/qiuzhen8484/COVID-DA
공식 코드COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
https://github.com/qliu24/SAKE
공식 코드Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
https://github.com/qy-feng/margin-openset
공식 코드https://github.com/qz-wang/act
공식 코드Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment
https://github.com/raamana/kernelmethods
공식 코드Kernel methods library for pattern analysis and machine learning in python
https://github.com/rabeehk/hyperformer
공식 코드Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
https://github.com/rafaelvareto/maximal-entropy-loss
공식 코드Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss
https://github.com/rafizunaed/domain_agnostic_content_aware_style_invariant
공식 코드https://github.com/rainjamesy/dida
공식 코드Dual-level Interaction for Domain Adaptive Semantic Segmentation
https://github.com/rakshith-2905/sista
공식 코드Target-Aware Generative Augmentations for Single-Shot Adaptation
https://github.com/ram95d/ovose
공식 코드OVOSE: Open-Vocabulary Semantic Segmentation in Event-Based Cameras
https://github.com/ramiMMhamza/Fed-Protoid
공식 코드Privacy-Preserving Adaptive Re-Identification without Image Transfer
https://github.com/ramimmhamza/s2p
공식 코드Source-Guided Similarity Preservation for Online Person Re-Identification
https://github.com/rango-zhang-hang/ntu-visual-recognition-via-vision-language-model-transfer
공식 코드M&M3D: Multi-Dataset Training and Efficient Network for Multi-view 3D Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.