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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/najaemin92/covi

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Contrastive Vicinal Space for Unsupervised Domain Adaptation

https://github.com/najaemin92/fixbi

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FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation

https://github.com/namisan/mt-dnn

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Multi-Task Deep Neural Networks for Natural Language Understanding

https://github.com/namkoong-lab/disde

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Diagnosing Model Performance Under Distribution Shift

https://github.com/namkoong-lab/llm-tabular-shifts

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LLM Embeddings Improve Test-time Adaptation to Tabular $Y|X$-Shifts

https://github.com/nancyquris/xdomainmix

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Cross-Domain Feature Augmentation for Domain Generalization

https://github.com/naoto0804/cross-domain-detection

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Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation

https://github.com/nardien/agent-distillation

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Distilling LLM Agent into Small Models with Retrieval and Code Tools

https://github.com/nardien/kala

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KALA: Knowledge-Augmented Language Model Adaptation

https://github.com/naver/oasis

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On the Road to Online Adaptation for Semantic Image Segmentation

https://github.com/naver/splade

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Parameter-Efficient Sparse Retrievers and Rerankers using Adapters

https://github.com/nazmul-karim170/C-SFDA_Source-Free-Domain-Adaptation

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C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation

https://github.com/neel-dey/anystar

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AnyStar: Domain randomized universal star-convex 3D instance segmentation

https://github.com/neerakara/test-time-adaptable-neural-networks-for-domain-generalization

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Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation

https://github.com/neerakara/tta-1d-density-matching

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A Field of Experts Prior for Adapting Neural Networks at Test Time

https://github.com/negin-ghamsarian/transformation-invariant-self-training-miccai23

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Domain Adaptation for Medical Image Segmentation using Transformation-Invariant Self-Training

https://github.com/nehamjain10/trudgcr

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Towards Realistic Underwater Dataset Generation and Color Restoration

https://github.com/nerdslab/latentdr

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LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration

https://github.com/neuro-ml/domain_shift_anatomy

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Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

https://github.com/neuropoly/domainadaptation

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Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

https://github.com/newbeeer/orthogonal_classifier

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Controlling Directions Orthogonal to a Classifier

https://github.com/nguyenthaivu/sadda

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Semi-Supervised Adversarial Discriminative Domain Adaptation

https://github.com/nicpopovic/fredo

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Few-Shot Document-Level Relation Extraction

https://github.com/nikosefth/crafting-shifts

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Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization

https://github.com/ninglab/ecellm

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eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data

https://github.com/nitaytech/docogen

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DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation

https://github.com/nka77/dahitra

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Large-scale Building Damage Assessment using a Novel Hierarchical Transformer Architecture on Satellite Images

https://github.com/nkdinsdale/sfharmony

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SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis

https://github.com/nle18/coref-llms

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Are Large Language Models Robust Coreference Resolvers?

https://github.com/nneurotransmitterr/tm-otta-snn

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Threshold Modulation for Online Test-Time Adaptation of Spiking Neural Networks

https://github.com/noahsark/dada

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Towards Improved Proxy-based Deep Metric Learning via Data-Augmented Domain Adaptation

https://github.com/nobias-project/domain-adaptive-trees

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Domain Adaptive Decision Trees: Implications for Accuracy and Fairness

https://github.com/nokia-bell-labs/papagei-foundation-model

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PaPaGei: Open Foundation Models for Optical Physiological Signals

https://github.com/noparkee/graph-clustering-based-dg

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Clustering-based Image-Text Graph Matching for Domain Generalization

https://github.com/nortondeclan/learning_beyond_experience

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Learning Beyond Experience: Generalizing to Unseen State Space with Reservoir Computing

https://github.com/npoe/covid-qa

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Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA

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