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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/OmarSZamzam/PixelShuffler

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PixelShuffler: A Simple Image Translation Through Pixel Rearrangement

https://github.com/OptimusPrimus/dcase2019_task1b

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Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification

https://github.com/PKU-EPIC/GAPartNet

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GAPartNet: Cross-Category Domain-Generalizable Object Perception and Manipulation via Generalizable and Actionable Parts

https://github.com/PRLAB21/COVID-19-Detection-System-using-Chest-X-Ray-Images

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COVID-19 Detection in Chest X-Ray Images using a New Channel Boosted CNN

https://github.com/PaddlePaddle/PaddleNLP/tree/develop/model_zoo/ernie-health

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Building Chinese Biomedical Language Models via Multi-Level Text Discrimination

https://github.com/Panda-Peter/visda2019-multisource

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Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019

https://github.com/Panda-Peter/visda2019-semisupervised

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Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019

https://github.com/PatrickHuembeli/Adversarial-Domain-Adaptation-for-Identifying-Phase-Transitions

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Identifying Quantum Phase Transitions with Adversarial Neural Networks

https://github.com/PaulaRuizB/FADE-ECG

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FADE: Forecasting for Anomaly Detection on ECG

https://github.com/Pei-KaiHuang/LDCformer_Ext

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Enhancing Learnable Descriptive Convolutional Vision Transformer for Face Anti-Spoofing

https://github.com/Peilun-Li/SG-GAN

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Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption

https://github.com/Pengchengpcx/Class-overwhelms-Mutual-Conditional-Blended-Target-Domain-Adaptation

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Class Overwhelms: Mutual Conditional Blended-Target Domain Adaptation

https://github.com/PengyiZhang/DRR4Covid

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DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs

https://github.com/PerForm-Lab-RIT/domain-adaptation-eye-tracking

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Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems

https://github.com/PrasannaPulakurthi/Foreground-Background-Augmentation

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Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data

https://github.com/PrasannaPulakurthi/SPM

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Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation

https://github.com/Pulkit-Khandelwal/medical-mldg-seg

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Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging

https://github.com/PythonOT/COOT

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CO-Optimal Transport

https://github.com/PythonOT/POT/blob/master/ot/da.py

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Optimal Transport for Multi-source Domain Adaptation under Target Shift

https://github.com/QData/TextAttack-A2T

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Towards Improving Adversarial Training of NLP Models

https://github.com/QingbeiGuo/SG-CNN

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Self-grouping Convolutional Neural Networks

https://github.com/QuwsarOhi/u-vectors

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U-vectors: Generating clusterable speaker embedding from unlabeled data

https://github.com/RAO-EPFL/DR-DA

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Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

https://github.com/RPIDIAL/SAMix

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Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation

https://github.com/RS-CSU/MemoryAdaptNet-master

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Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory

https://github.com/RS2002/KNN-MMD

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KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment

https://github.com/ReNginx/ADeLA

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ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation

https://github.com/ReijiSoftmaxSaito/ADSI

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Domain Generalization through Attenuation of Domain-Specific Information

https://github.com/Renchunzi-Xie/AOMSDA

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Automatic Online Multi-Source Domain Adaptation

https://github.com/Renchunzi-Xie/GearNet

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GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

https://github.com/RenzKa/sign-segmentation

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Sign Segmentation with Changepoint-Modulated Pseudo-Labelling

https://github.com/Rh-YE/ai4galmorph_desi

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From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption

https://github.com/RockySJ/WDGRL

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Wasserstein Distance Guided Representation Learning for Domain Adaptation

https://github.com/RogerZhangzz/CAG_UDA

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Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation

https://github.com/RomainMsrd/UniJDOT

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Optimal Transport and Adaptive Thresholding for Universal Domain Adaptation on Time Series

https://github.com/RoyalVane/ASM

공식 코드

Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation

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