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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/lizhitwo/ConfidenceEstimates

공식 코드

Improving Confidence Estimates for Unfamiliar Examples

https://github.com/lizw14/super-clevr

공식 코드

Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning

https://github.com/ljjcoder/PCL

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Probabilistic Contrastive Learning for Domain Adaptation

https://github.com/ljjcoder/ehtdi

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Exploring High-quality Target Domain Information for Unsupervised Domain Adaptive Semantic Segmentation

https://github.com/ljjcoder/probabilistic-contrastive-learning

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Probabilistic Contrastive Learning for Domain Adaptation

https://github.com/lkpengcs/dcda

공식 코드

Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning

https://github.com/lkpengcs/rvms

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Student Becomes Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation

https://github.com/lloongx/sfda-cbmt

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Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher

https://github.com/lluckyyh/vt-ada

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Vision Transformer-based Adversarial Domain Adaptation

https://github.com/lmz123321/which_invariance

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Which Invariance Should We Transfer? A Causal Minimax Learning Approach

https://github.com/locluclak/si-seqfs-da

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Statistical Inference for Sequential Feature Selection after Domain Adaptation

https://github.com/locuslab/tta_conjugate

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Test-Time Adaptation via Conjugate Pseudo-labels

https://github.com/lofrienger/curri-afda

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Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation

https://github.com/lofrienger/single_surgicalscene_for_segmentation

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Rethinking Surgical Instrument Segmentation: A Background Image Can Be All You Need

https://github.com/lojzezust/slr

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Learning with Weak Annotations for Robust Maritime Obstacle Detection

https://github.com/lonelyhope/dada

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Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution

https://github.com/longshaocong/dgmamba

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DGMamba: Domain Generalization via Generalized State Space Model

https://github.com/longshaocong/dmda

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Rethinking Domain Generalization: Discriminability and Generalizability

https://github.com/louey233/toward-mutual-information

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Unsupervised Domain Adaptation for Cardiac Segmentation: Towards Structure Mutual Information Maximization

https://github.com/loyoyang/decota

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Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation

https://github.com/lr94/abas

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Adversarial Branch Architecture Search for Unsupervised Domain Adaptation

https://github.com/lrank/Domain_Robust_Text_Representation

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What's in a Domain? Learning Domain-Robust Text Representations using Adversarial Training

https://github.com/lsky96/unrolled-tbsca-ad

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Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling

https://github.com/lslrh/T-SVDNet

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T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation

https://github.com/lsmcolab/semantic-segmentation-under-adverse-conditions

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Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach

https://github.com/lts5/distill-soda

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Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

https://github.com/lts5/proto-sf-osda

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Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

https://github.com/lucasfernando-aes/ms-dial

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Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers

https://github.com/lucasmansilla/DGvGS

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Domain Generalization via Gradient Surgery

https://github.com/luckycallor/CDAAE

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Cross-Domain Adversarial Auto-Encoder

https://github.com/lugroupumn/icl-ellipticpdes

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In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning

https://github.com/lugroupumn/icl_linear_systems

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In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning

https://github.com/lugruber/dre_iter_reg

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Overcoming Saturation in Density Ratio Estimation by Iterated Regularization

https://github.com/lukashedegaard/dage

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Supervised Domain Adaptation using Graph Embedding

https://github.com/lukemelas/pixmatch

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PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training

https://github.com/luo-junyu/gala

공식 코드

GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation

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