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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/huitangtang/SRDC-CVPR2020

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Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering

https://github.com/huitangtang/SRDCPP

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Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering

https://github.com/huitangtang/disclusterda

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Unsupervised Domain Adaptation via Distilled Discriminative Clustering

https://github.com/huitangtang/on_the_utility_of_synthetic_data

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A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation

https://github.com/hukenovs/easyportrait

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EasyPortrait -- Face Parsing and Portrait Segmentation Dataset

https://github.com/human-analysis/tradeoff-invariant-representation-learning

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On Characterizing the Trade-off in Invariant Representation Learning

https://github.com/hust-linyi/seg_trick

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Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions

https://github.com/hydropml/disasternets

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DisasterNets: Embedding Machine Learning in Disaster Mapping

https://github.com/hyeonwoocho7/Cell_Detection-MICCAI

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Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap

https://github.com/hyeonwoocho7/UDA-Cell-Detection

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Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection

https://github.com/hyeonwoocho7/cng-sfda

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CNG-SFDA:Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation

https://github.com/hygenie1228/cycleadapt_release

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Cyclic Test-Time Adaptation on Monocular Video for 3D Human Mesh Reconstruction

https://github.com/hytseng0509/CrossDomainFewShot

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Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation

https://github.com/hytseng0509/DropGrad

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Regularizing Meta-Learning via Gradient Dropout

https://github.com/iCGY96/APR

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Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain

https://github.com/iamalexkorotin/Wasserstein2GenerativeNetworks

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Wasserstein-2 Generative Networks

https://github.com/ianxmason/compositional-robustness

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Modularity Trumps Invariance for Compositional Robustness

https://github.com/ibm-aur-nlp/domain-specific-QA

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Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension

https://github.com/ibm/amr-annotations

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Maximum Bayes Smatch Ensemble Distillation for AMR Parsing

https://github.com/ibm/kgi-slot-filling

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Robust Retrieval Augmented Generation for Zero-shot Slot Filling

https://github.com/ibtihalferwana/pirm

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Learning Optimal Features via Partial Invariance

https://github.com/iccv2595/svdp

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Exploring Sparse Visual Prompt for Domain Adaptive Dense Prediction

https://github.com/iceli1007/3d-adapter

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One-shot Generative Domain Adaptation in 3D GANs

https://github.com/icemiliang/pyvot

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Regularized Wasserstein Means for Aligning Distributional Data

https://github.com/icemiliang/vot

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Variational Wasserstein Clustering

https://github.com/icey-zhang/DIAnet

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Distribution-aware Interactive Attention Network and Large-scale Cloud Recognition Benchmark on FY-4A Satellite Image

https://github.com/ictnlp/PTE-NMT

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Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation

https://github.com/ictnlp/lfr-nmt

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Continual Learning of Neural Machine Translation within Low Forgetting Risk Regions

https://github.com/idanachi/DefRec_and_PCM

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Self-Supervised Learning for Domain Adaptation on Point-Clouds

https://github.com/idea-iitd/grail

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GRAIL: Graph Edit Distance and Node Alignment Using LLM-Generated Code

https://github.com/idealwei/ASANet

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Affinity Space Adaptation for Semantic Segmentation Across Domains

https://github.com/idejie/3DSyn

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3D Vision and Language Pretraining with Large-Scale Synthetic Data

https://github.com/ieee8023/xray-generalization

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On the limits of cross-domain generalization in automated X-ray prediction

https://github.com/iexarchos/PolicyTransferKinDRA

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Policy Transfer via Kinematic Domain Randomization and Adaptation

https://github.com/ifnspaml/UBNA

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Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method for Semantic Segmentation Without Using Source Domain Representations

https://github.com/ifnspaml/cbna

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Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation

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