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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/diffusion-classifier/diffusion-classifier

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Your Diffusion Model is Secretly a Zero-Shot Classifier

https://github.com/dingjiansw101/hgformer

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HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation

https://github.com/dingmyu/DAPN

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Domain-Adaptive Few-Shot Learning

https://github.com/dingthuang/sd-net

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SD-Net: Symmetric-Aware Keypoint Prediction and Domain Adaptation for 6D Pose Estimation In Bin-picking Scenarios

https://github.com/dirkiedai/sk-mt

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Simple and Scalable Nearest Neighbor Machine Translation

https://github.com/div290/dadee

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DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs

https://github.com/divelab/atta

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Active Test-Time Adaptation: Theoretical Analyses and An Algorithm

https://github.com/divyakraman/salad_sourcefreeactivelabelagnosticdomainadaptation

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SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and Detection

https://github.com/djidje/D-MMD

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Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification

https://github.com/dkaizhang/emdcfl

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Clustered Federated Learning via Embedding Distributions

https://github.com/dliu5812/PDAM

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Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting

https://github.com/dliu5812/ddf

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Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

https://github.com/dmirlab-group/gca

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Transferable Time-Series Forecasting under Causal Conditional Shift

https://github.com/dmirlab-group/lca

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Time Series Domain Adaptation via Latent Invariant Causal Mechanism

https://github.com/dmlc/gluon-cv

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Bag of Tricks for Image Classification with Convolutional Neural Networks

https://github.com/dmn-sjk/ar-tta

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AR-TTA: A Simple Method for Real-World Continual Test-Time Adaptation

https://github.com/dnap512/SelfReg

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SelfReg: Self-supervised Contrastive Regularization for Domain Generalization

https://github.com/donghao51/aeo

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Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive Entropy-aware Optimization

https://github.com/donghao51/awesome-multimodal-adaptation

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Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models

https://github.com/donghao51/moosa

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Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision

https://github.com/donghao51/simmmdg

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SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

https://github.com/dongkwani/upcsc

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Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization

https://github.com/donglao/PCEDA

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Phase Consistent Ecological Domain Adaptation

https://github.com/dongliangchang/Mutual-to-Separate

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Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation

https://github.com/dosowiechi/clipartt

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CLIPArTT: Adaptation of CLIP to New Domains at Test Time

https://github.com/dosowiechi/clust3

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ClusT3: Information Invariant Test-Time Training

https://github.com/dosowiechi/mlmp

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Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation

https://github.com/dotrannhattuong/ECB

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Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation

https://github.com/dpernes/modafm

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Tackling unsupervised multi-source domain adaptation with optimism and consistency

https://github.com/dpfried/rnng-bert

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Cross-Domain Generalization of Neural Constituency Parsers

https://github.com/dr-costas/undaw

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Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification

https://github.com/drgriffis/NeuralVecmap

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Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility

https://github.com/dritter-bht/synthnet-transfer-learning

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CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification

https://github.com/drluo/semiets

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SemiETS: Integrating Spatial and Content Consistencies for Semi-Supervised End-to-end Text Spotting

https://github.com/drugd/msda

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Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening

https://github.com/drumpt/sgem

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SGEM: Test-Time Adaptation for Automatic Speech Recognition via Sequential-Level Generalized Entropy Minimization

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