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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/computational-cell-analytics/probabilistic-domain-adaptation

공식 코드

Probabilistic Domain Adaptation for Biomedical Image Segmentation

https://github.com/congxin95/DaFeC

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Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering

https://github.com/continental/image-statistics-matching

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Keep it Simple: Image Statistics Matching for Domain Adaptation

https://github.com/cookielee77/DAST

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Domain Adaptive Text Style Transfer

https://github.com/copenlu/TESTED/blob/main/README.md

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Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection

https://github.com/copenlu/cite-worth

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CiteWorth: Cite-Worthiness Detection for Improved Scientific Document Understanding

https://github.com/copenlu/xformer-multi-source-domain-adaptation

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Transformer Based Multi-Source Domain Adaptation

https://github.com/cps11/darl

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Reinforcement Learning for Few-Shot Text Generation Adaptation

https://github.com/cr-copec/cr-copec

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CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports

https://github.com/crazorback/aadg

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AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation

https://github.com/criteo-research/CausE

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Causal Embeddings for Recommendation

https://github.com/cs20s030/fusdom

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FusDom: Combining In-Domain and Out-of-Domain Knowledge for Continuous Self-Supervised Learning

https://github.com/cscyqj/miccai23-protocontra-sfda

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Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning

https://github.com/csehong/SSPP-DAN

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SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person

https://github.com/csiro-robotics/WildScenes

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WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environments

https://github.com/csiro-robotics/p-gat

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Pose-Graph Attentional Graph Neural Network for Lidar Place Recognition

https://github.com/csmliu/adanec

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Adaptive Network Combination for Single-Image Reflection Removal: A Domain Generalization Perspective

https://github.com/cswin/AWC

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CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation

https://github.com/cswin/cada

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CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

https://github.com/cszhangzhen/graphcta

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Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation

https://github.com/ctu-vras/t-uda

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T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds

https://github.com/cuishuhao/BNM

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Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations

https://github.com/cuishuhao/GVB

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Gradually Vanishing Bridge for Adversarial Domain Adaptation

https://github.com/cuishuhao/HDA

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Heuristic Domain Adaptation

https://github.com/cuthalionn/prompter

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Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation

https://github.com/cuttle-fish-my/gm-guided-dg

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Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation

https://github.com/cuzyoung/crossearth

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CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation

https://github.com/cv-rits/densemtl

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DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning

https://github.com/cv-shuchanglyu/sam-joanet

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Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model

https://github.com/cv516buaa/mmotu_ds2net

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MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

https://github.com/cvi2snt/dda-mlic

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Discriminator-free Unsupervised Domain Adaptation for Multi-label Image Classification

https://github.com/cvlab-unibo/d4-dbst

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Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation

https://github.com/cvlab-unibo/mm2d3d

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Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation

https://github.com/cvlab-unibo/refrec

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RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation

https://github.com/cvlab-unibo/shallow_da

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Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries

https://github.com/cvmi-lab/doda

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DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation

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