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Domain Adaptation

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하위 태스크: 13
평가표: 58
보존된 평가 기록: 391

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:

A Brief Review of Domain Adaptation

<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/chamwen/MEKT

공식 코드

Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces

https://github.com/chandrasg/lexica

공식 코드

Understanding and Measuring Psychological Stress using Social Media

https://github.com/chaneyddtt/UDA-Animal-Pose

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From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation

https://github.com/chaneyddtt/scarcenet

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ScarceNet: Animal Pose Estimation with Scarce Annotations

https://github.com/changdaeoh/farconvae

공식 코드

Learning Fair Representation via Distributional Contrastive Disentanglement

https://github.com/changliu00/causal-semantic-generative-model

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Learning Causal Semantic Representation for Out-of-Distribution Prediction

https://github.com/changmenseng/semantic_tree

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Interpreting Sentiment Composition with Latent Semantic Tree

https://github.com/changwxx/uniot-for-unida

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Unified Optimal Transport Framework for Universal Domain Adaptation

https://github.com/chaostheproducer/ttt4d

공식 코드

Test Time Training for 4D Medical Image Interpolation

https://github.com/chaytonmin/occupancy-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/chaytonmin/voxel-mae

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Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders

https://github.com/checkstep/mole-stance

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Cross-Domain Label-Adaptive Stance Detection

https://github.com/chehx/dgdr

공식 코드

Towards Generalizable Diabetic Retinopathy Grading in Unseen Domains

https://github.com/chen-ziyang/grata

공식 코드

Gradient Alignment Improves Test-Time Adaptation for Medical Image Segmentation

https://github.com/chen-ziyang/trid

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Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation

https://github.com/chen-ziyang/vptta

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Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation

https://github.com/chen742/DCF

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Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation

https://github.com/chen742/PiPa

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PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation

https://github.com/chenchao666/HoMM-Master

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HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation

https://github.com/chenchao666/JDDA-Master

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Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation

https://github.com/cheng-01037/causality-medical-image-domain-generalization

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Causality-inspired Single-source Domain Generalization for Medical Image Segmentation

https://github.com/chengchunhsu/EveryPixelMatters

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Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector

https://github.com/chengcv/dfrfs

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Disentangled Feature Representation for Few-shot Image Classification

https://github.com/chengyui/nuwats

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NuwaTS: a Foundation Model Mending Every Incomplete Time Series

https://github.com/chenhao2345/ABMT

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Enhancing Diversity in Teacher-Student Networks via Asymmetric branches for Unsupervised Person Re-identification

https://github.com/chenhao2345/GCL

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Joint Generative and Contrastive Learning for Unsupervised Person Re-identification

https://github.com/chenhao2345/ucr

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Unsupervised Lifelong Person Re-identification via Contrastive Rehearsal

https://github.com/chenhong-zhou/krada

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KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation

https://github.com/chenxi52/upa

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Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation

https://github.com/chester256/mcl

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Multi-level Consistency Learning for Semi-supervised Domain Adaptation

https://github.com/chingyaoc/estimating-generalization

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Estimating Generalization under Distribution Shifts via Domain-Invariant Representations

https://github.com/chingyaoc/infoot

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InfoOT: Information Maximizing Optimal Transport

https://github.com/chingyaoc/mdm

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The Role of Embedding Complexity in Domain-invariant Representations

https://github.com/chl8856/survite

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SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

https://github.com/chma1024/densepass

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DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange

https://github.com/chojw/maximum-classifier-discrepancy-for-active-learning

공식 코드

MCDAL: Maximum Classifier Discrepancy for Active Learning

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