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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/ifnspaml/revt

공식 코드

A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation

https://github.com/iit-pavis/learnaug-uda

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Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation

https://github.com/im-ethz/pub-gdu4dg

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Gated Domain Units for Multi-source Domain Generalization

https://github.com/imatif17/Prototype-Mean-Teacher

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Multi-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher

https://github.com/imatif17/acia

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Attention-based Class-Conditioned Alignment for Multi-Source Domain Adaptation of Object Detectors

https://github.com/inhwanbae/singulartrajectory

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SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model

https://github.com/ink-usc/fewner

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Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER

https://github.com/ink-usc/sparse-distillation

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Sparse Distillation: Speeding Up Text Classification by Using Bigger Student Models

https://github.com/inouye-lab/alignment-upper-bound

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Cooperative Distribution Alignment via JSD Upper Bound

https://github.com/inouye-lab/feddg_benchmark

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Benchmarking Algorithms for Federated Domain Generalization

https://github.com/inouye-lab/saub

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Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization

https://github.com/intelligentmachines-itu/domain-adaptive-building-counting

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Cross-Region Building Counting in Satellite Imagery using Counting Consistency

https://github.com/invictus717/UniDG

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Towards Unified and Effective Domain Generalization

https://github.com/iremeyiokur/multipie_extended_ear_dataset

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Multimodal Age and Gender Classification Using Ear and Profile Face Images

https://github.com/ismailnejjar/RRDA

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Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework

https://github.com/ismailnejjar/dare-gram

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DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

https://github.com/ismhinolab/gda_via_cnf

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Gradual Domain Adaptation via Normalizing Flows

https://github.com/ispc-lab/bmd

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BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation

https://github.com/ispc-lab/glc

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Upcycling Models under Domain and Category Shift

https://github.com/ispc-lab/glc-plus

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

https://github.com/ispc-lab/lead

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LEAD: Learning Decomposition for Source-free Universal Domain Adaptation

https://github.com/itay-nakash/AdaptiVocab

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AdaptiVocab: Enhancing LLM Efficiency in Focused Domains through Lightweight Vocabulary Adaptation

https://github.com/iurada/fairness_crossdomain

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Fairness meets Cross-Domain Learning: a new perspective on Models and Metrics

https://github.com/ivn-rin/bio-med-bit

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Localising In-Domain Adaptation of Transformer-Based Biomedical Language Models

https://github.com/iyanush/nollysenti

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NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification

https://github.com/izecson/terminology-translation

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Rule-based Morphological Inflection Improves Neural Terminology Translation

https://github.com/izhx/CLasDA

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Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition

https://github.com/jacklangerman/dance

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Domain Adaptation of Networks for Camera Pose Estimation: Learning Camera Pose Estimation Without Pose Labels

https://github.com/jacky121298/wlst

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WLST: Weak Labels Guided Self-training for Weakly-supervised Domain Adaptation on 3D Object Detection

https://github.com/jackyzyb/autofedgp

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Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting

https://github.com/jacobzhaoziyuan/MT-UDA

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MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels

https://github.com/jacobzhaoziyuan/le-uda

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LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation

https://github.com/jaeill/CVPR23-VNE

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VNE: An Effective Method for Improving Deep Representation by Manipulating Eigenvalue Distribution

https://github.com/jaiprakash1824/vlm_adv_attack

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Demonstration of an Adversarial Attack Against a Multimodal Vision Language Model for Pathology Imaging

https://github.com/jakobabesser/icassp_2022_disentanglementasc

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Towards Audio Domain Adaptation for Acoustic Scene Classification using Disentanglement Learning

https://github.com/jalilnkh/kartalol-nir-isl2021031301

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KartalOl: Transfer learning using deep neural network for iris segmentation and localization: New dataset for iris segmentation

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