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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/HsinYingLee/DRIT

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Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/HzFu/COVID19_imaging_AI_paper_list

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Towards Data-Efficient Learning: A Benchmark for COVID-19 CT Lung and Infection Segmentation

https://github.com/Hzzone/MTLFace

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When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework

https://github.com/IBM/OoD

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Treatment Effect Estimation using Invariant Risk Minimization

https://github.com/IBM/transition-amr-parser

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Maximum Bayes Smatch Ensemble Distillation for AMR Parsing

https://github.com/IGNF/FLAIR-1-AI-Challenge

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FLAIR #1: semantic segmentation and domain adaptation dataset

https://github.com/INZHAGY/WDA-Net

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WDA-Net: Weakly-Supervised Domain Adaptive Segmentation of Electron Microscopy

https://github.com/IPL-UV/pvl8dagans

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Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

https://github.com/ISorokos/SafeML

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SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

https://github.com/IdanAchituve/DefRec_and_PCM

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Self-Supervised Learning for Domain Adaptation on Point-Clouds

https://github.com/IssamLaradji/M-ADDA

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M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning

https://github.com/Ivsucram/ACDC

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ACDC: Online Unsupervised Cross-Domain Adaptation

https://github.com/JDAI-CV/FADA

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Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation

https://github.com/JHL-HUST/ATDA

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Improving the Generalization of Adversarial Training with Domain Adaptation

https://github.com/JTRNEO/SynRS3D

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SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery

https://github.com/JabezNzomo99/claim-matching-robustness

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When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits

https://github.com/Jack-Poole/Statistic-Alignment

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On statistic alignment for domain adaptation in structural health monitoring

https://github.com/JasonForJoy/SA-BERT

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Speaker-Aware BERT for Multi-Turn Response Selection in Retrieval-Based Chatbots

https://github.com/JeremyCJM/CCST

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Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer

https://github.com/JeremyXSC/MCN-MT

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Learning from Self-Discrepancy via Multiple Co-teaching for Cross-Domain Person Re-Identification

https://github.com/Jhyun17/DeYO

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Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors

https://github.com/JialingRichard/Digital-Twin-Fault-Diagnosis

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A domain adaptation neural network for digital twin-supported fault diagnosis

https://github.com/JianghaoWu/FPL-UDA

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CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

https://github.com/Jiaolong/self-supervised-da

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Self-supervised Domain Adaptation for Computer Vision Tasks

https://github.com/Jiaolong/ss-da-consistency

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Self-Supervised Domain Adaptation with Consistency Training

https://github.com/JigglypuffStitch/AdaFreq

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Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification

https://github.com/JingWang18/Discriminative-Feature-Alignment

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Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

https://github.com/JingbiaoMei/RGCL

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Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection

https://github.com/JitengMu/Learning-from-Synthetic-Animals

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Learning from Synthetic Animals

https://github.com/Jonas1302/anomalib

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Domain-independent detection of known anomalies

https://github.com/JorisRoels/domain-adaptive-segmentation

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Domain Adaptive Segmentation in Volume Electron Microscopy Imaging

https://github.com/JoseLGomez/Co-training_SemSeg_UDA

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Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

https://github.com/JudgingH/PEGO

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Learn to Preserve and Diversify: Parameter-Efficient Group with Orthogonal Regularization for Domain Generalization

https://github.com/Juliusvk/Semi-Generative-Modelling

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Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features

https://github.com/Jun-Pu/UCOS-DA

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Unsupervised Camouflaged Object Segmentation as Domain Adaptation

https://github.com/JunMa11/HM_DataAug

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Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.