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Domain Adaptation

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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/BBillot/SynthSeg

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SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining

https://github.com/BIT-DA/BCDM

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Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

https://github.com/BIT-DA/DDA

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Dynamic Domain Adaptation for Efficient Inference

https://github.com/BIT-DA/GDCAN

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Generalized Domain Conditioned Adaptation Network

https://github.com/BIT-DA/ParetoDA

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Pareto Domain Adaptation

https://github.com/BIT-DA/SSAN

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Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation

https://github.com/BIT-DA/TSA

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Transferable Semantic Augmentation for Domain Adaptation

https://github.com/BOBrown/JADF-caffe

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Joint Distribution Alignment via Adversarial Learning for Domain Adaptive Object Detection

https://github.com/BPYap/BERT-WSD

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Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences

https://github.com/BUserName/PGL

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Progressive Graph Learning for Open-Set Domain Adaptation

https://github.com/Bala93/CLIPCalib

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Robust Calibration of Large Vision-Language Adapters

https://github.com/BaoshunWq/clip_TAEA

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Test-time Alignment-Enhanced Adapter for Vision-Language Models

https://github.com/BashivanLab/afd

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Adversarial Feature Desensitization

https://github.com/BayesWatch/deep-kernel-transfer

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Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels

https://github.com/BestiVictory/ILGnet

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ILGNet: Inception Modules with Connected Local and Global Features for Efficient Image Aesthetic Quality Classification using Domain Adaptation

https://github.com/BiQiWHU/CMFormer

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Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation

https://github.com/BoChen-Daniel/Expert-Linking

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CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

https://github.com/BorealisAI/cross_domain_coherence

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A Cross-Domain Transferable Neural Coherence Model

https://github.com/BorisMuzellec/SubspaceOT

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Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections

https://github.com/Boyiliee/MoEx

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On Feature Normalization and Data Augmentation

https://github.com/BoyuanJiang/PTELM

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Parameter Transfer Extreme Learning Machine based on Projective Model

https://github.com/Britefury/self-ensemble-visual-domain-adapt

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Self-ensembling for visual domain adaptation

https://github.com/ByeongHyunPak/tqdm

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Textual Query-Driven Mask Transformer for Domain Generalized Segmentation

https://github.com/CARG-uOttawa/DrIFT

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DrIFT: Autonomous Drone Dataset with Integrated Real and Synthetic Data, Flexible Views, and Transformed Domains

https://github.com/CDAMICCAI2019/CDA

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Constrained domain adaptation for Image segmentation

https://github.com/CEA-LIST/adv-sat

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Optimal Transport as a Defense Against Adversarial Attacks

https://github.com/CSAILVision/semantic-segmentation-pytorch

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Sim2Real for Self-Supervised Monocular Depth and Segmentation

https://github.com/CTI-VISION/Mirror-Sample

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Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment

https://github.com/CVLAB-Unibo/ATDT

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Learning Across Tasks and Domains

https://github.com/CVLAB-Unibo/Unsupervised_Depth_Adaptation

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Unsupervised Domain Adaptation for Depth Prediction from Images

https://github.com/CVMI-Lab/ST3D

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ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

https://github.com/CVRL/domain-specific-BSIF-for-iris-recognition

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Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition

https://github.com/Captain-Hong/UDA-via-joint-adversarial-learning-and-self-learning

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Unsupervised domain adaptation for cross-modality liver segmentation via joint adversarial learning and self-learning

https://github.com/Cardio-AI/3d-mri-domain-adaptation

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Unsupervised Domain Adaptation from Axial to Short-Axis Multi-Slice Cardiac MR Images by Incorporating Pretrained Task Networks

https://github.com/Cavin-Lee/PAS

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Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces

https://github.com/Chaolei98/FreeZAD

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Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models

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