SOTAVerified

Domain Adaptation

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:

( Image credit: Unsupervised Image-to-Image Translation Networks )

Papers

Showing 1–50 of 6439 papers

TitleStatusHype
MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training StrategiesCode9
Knowledge-tuning Large Language Models with Structured Medical Knowledge Bases for Reliable Response Generation in ChineseCode4
PyTorch AdaptCode4
A Comprehensive Survey on Test-Time Adaptation under Distribution ShiftsCode3
A Review of Single-Source Deep Unsupervised Visual Domain AdaptationCode3
Revisiting, Benchmarking and Understanding Unsupervised Graph Domain AdaptationCode3
FDA: Fourier Domain Adaptation for Semantic SegmentationCode3
Generative Data Augmentation using LLMs improves Distributional Robustness in Question AnsweringCode3
Language Models are Few-Shot LearnersCode3
PyGDA: A Python Library for Graph Domain AdaptationCode3
Parametric Retrieval Augmented GenerationCode3
Unified Source-Free Domain AdaptationCode3
Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised ModelsCode3
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation ModelsCode3
Proxy Denoising for Source-Free Domain AdaptationCode3
Towards Building Multilingual Language Model for MedicineCode3
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified FlowCode3
Open-Set Domain Adaptation for Semantic SegmentationCode2
Multi-Representation Adaptation Network for Cross-domain Image ClassificationCode2
Multi-scale Quaternion CNN and BiGRU with Cross Self-attention Feature Fusion for Fault Diagnosis of BearingCode2
MIC: Masked Image Consistency for Context-Enhanced Domain AdaptationCode2
Implicit Neural Representation in Medical Imaging: A Comparative SurveyCode2
HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic SegmentationCode2
HyperGAN-CLIP: A Unified Framework for Domain Adaptation, Image Synthesis and ManipulationCode2
LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process ThinkingCode2
Medical Image Segmentation with Domain Adaptation: A SurveyCode2
Mind the Domain Gap: a Systematic Analysis on Bioacoustic Sound Event DetectionCode2
PolarMix: A General Data Augmentation Technique for LiDAR Point CloudsCode2
Foundational Large Language Models for Materials ResearchCode2
OpenESS: Event-based Semantic Scene Understanding with Open VocabulariesCode2
EgoVideo: Exploring Egocentric Foundation Model and Downstream AdaptationCode2
Generative Adversarial Network in Medical Imaging: A ReviewCode2
Domain Adaptation with a Single Vision-Language EmbeddingCode2
DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical AlignmentCode2
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future DirectionsCode2
Domain Adaptive and Generalizable Network Architectures and Training Strategies for Semantic Image SegmentationCode2
Continual Test-Time Domain AdaptationCode2
Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image SegmentationCode2
ConvLoRA and AdaBN based Domain Adaptation via Self-TrainingCode2
Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic DataCode2
CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive SurveyCode2
Denoising as Adaptation: Noise-Space Domain Adaptation for Image RestorationCode2
Earth-Adapter: Bridge the Geospatial Domain Gaps with Mixture of Frequency AdaptationCode2
Three New Validators and a Large-Scale Benchmark Ranking for Unsupervised Domain AdaptationCode2
CodeS: Towards Building Open-source Language Models for Text-to-SQLCode2
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic SegmentationCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMsCode2
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point CloudsCode2
Adversarial Open Domain Adaptation for Sketch-to-Photo SynthesisCode2
Show:102550
← PrevPage 1 of 129Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FFTATAverage Accuracy96—Unverified
2PMTransAverage Accuracy95.3—Unverified
3CMKDAverage Accuracy94.4—Unverified
4SSRT-B (ours)Average Accuracy93.5—Unverified
5CDTransAverage Accuracy92.6—Unverified
6CoViAverage Accuracy91.8—Unverified
7GSDEAverage Accuracy91.7—Unverified
8FixBiAverage Accuracy91.4—Unverified
9Contrastive Adaptation NetworkAverage Accuracy90.6—Unverified
10BIWAAAverage Accuracy90.5—Unverified
#ModelMetricClaimedVerifiedStatus
1HALOmIoU78.1—Unverified
2ILM-ASSLmIoU76.6—Unverified
3DCFmIoU69.3—Unverified
4HRDA+PiPamIoU68.2—Unverified
5MICmIoU67.3—Unverified
6FREDOM - TransformermIoU67—Unverified
7HRDAmIoU65.8—Unverified
8SePiComIoU64.3—Unverified
9MIC + Guidance TrainingmIoU63.8—Unverified
10DAFormer + ProCSTmIoU61.6—Unverified
#ModelMetricClaimedVerifiedStatus
1HALOmIoU77.8—Unverified
2DCFmIoU77.7—Unverified
3ILM-ASSLmIoU76.1—Unverified
4MICmIoU75.9—Unverified
5HRDA+PiPamIoU75.6—Unverified
6HRDAmIoU73.8—Unverified
7FREDOM - TransformermIoU73.6—Unverified
8HALOmIoU73.3—Unverified
9SePiComIoU70.3—Unverified
10DAFormer + ProCSTmIoU69.4—Unverified
#ModelMetricClaimedVerifiedStatus
1SWGAccuracy92.3—Unverified
2RCLAccuracy90—Unverified
3PGA (ViT-L/14)Accuracy89.4—Unverified
4CMKDAccuracy89—Unverified
5PMTransAccuracy89—Unverified
6MICAccuracy86.2—Unverified
7PGA (ViT-B/16)Accuracy85.1—Unverified
8ELSAccuracy84.6—Unverified
9SDAT (ViT-B/16)Accuracy84.3—Unverified
10CDTrans (DeiT-B)Accuracy80.5—Unverified
#ModelMetricClaimedVerifiedStatus
1FFTATAccuracy93.8—Unverified
2RCLAccuracy93.2—Unverified
3MICAccuracy92.8—Unverified
4SWGAccuracy92.7—Unverified
5CMKDAccuracy91.8—Unverified
6DePTAccuracy90.7—Unverified
7SDAT(ViT)Accuracy89.8—Unverified
8SFDA2++Accuracy89.6—Unverified
9PMtransAccuracy88.8—Unverified
10CoViAccuracy88.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CMKDAccuracy94.3—Unverified
2MCC+NWDAccuracy90.7—Unverified
3GLOT-DRAccuracy90.4—Unverified
4SPLAccuracy90.3—Unverified
5DFA-SAFNAccuracy90.2—Unverified
6DADAAccuracy89.3—Unverified
7DFA-ENTAccuracy89.1—Unverified
8DDAAccuracy88.9—Unverified
9MEDMAccuracy88.9—Unverified
10IAFN+ENTAccuracy88.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SoRAmIoU78.8—Unverified
2ReinmIoU77.6—Unverified
3CoDAmIoU72.6—Unverified
4Refign (HRDA)mIoU72.1—Unverified
5HALOmIoU71.9—Unverified
6MICmIoU70.4—Unverified
7HRDAmIoU68—Unverified
8Refign (DAFormer)mIoU65.5—Unverified
9VBLC (DAFormer)mIoU64.2—Unverified
10CMFormermIoU60.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FACTAccuracy98.8—Unverified
2FAMCDAccuracy98.72—Unverified
3DFA-MCDAccuracy98.6—Unverified
4Mean teacherAccuracy98.26—Unverified
5DRANetAccuracy98.2—Unverified
6SHOTAccuracy98—Unverified
7DFA-ENTAccuracy97.9—Unverified
8CyCleGAN (Light-weight Calibrator)Accuracy97.1—Unverified