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 601–650 of 6439 papers

TitleStatusHype
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Neutral residues: revisiting adapters for model extension—0
DyMix: Dynamic Frequency Mixup Scheduler based Unsupervised Domain Adaptation for Enhancing Alzheimer's Disease Identification—0
DAViD: Domain Adaptive Visually-Rich Document Understanding with Synthetic Insights—0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
OSSA: Unsupervised One-Shot Style AdaptationCode1
DoPAMine: Domain-specific Pre-training Adaptation from seed-guided data Mining—0
DCAST: Diverse Class-Aware Self-Training Mitigates Selection Bias for Fairer LearningCode0
IDEA: An Inverse Domain Expert Adaptation Based Active DNN IP Protection Method—0
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain AdaptationCode0
Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation—0
BiPC: Bidirectional Probability Calibration for Unsupervised Domain AdaptionCode0
Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework—0
Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment—0
Reducing Semantic Ambiguity In Domain Adaptive Semantic Segmentation Via Probabilistic Prototypical Pixel ContrastCode0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Prompt-Driven Temporal Domain Adaptation for Nighttime UAV TrackingCode1
DRL-STNet: Unsupervised Domain Adaptation for Cross-modality Medical Image Segmentation via Disentangled Representation Learning—0
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection—0
LLM4Brain: Training a Large Language Model for Brain Video Understanding—0
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations—0
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting—0
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularizationCode1
Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation—0
Source-Free Domain Adaptation for YOLO Object DetectionCode2
Unsupervised Attention Regularization Based Domain Adaptation for Oracle Character Recognition—0
LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM—0
Layer-wise Model Merging for Unsupervised Domain Adaptation in Segmentation Tasks—0
Multilingual Transfer and Domain Adaptation for Low-Resource Languages of Spain—0
EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition—0
Quantifying Context Bias in Domain Adaptation for Object Detection—0
UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized FrameworkCode1
FUSED-Net: Detecting Traffic Signs with Limited Data—0
An Effective Approach to Embedding Source Code by Combining Large Language and Sentence Embedding Models—0
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks—0
LLMs are One-Shot URL Classifiers and Explainers—0
MSSDA: Multi-Sub-Source Adaptation for Diabetic Foot Neuropathy Recognition—0
Unsupervised Attention-Based Multi-Source Domain Adaptation Framework for Drift Compensation in Electronic Nose Systems—0
LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR—0
Unsupervised Domain Adaptation for Keyphrase Generation using Citation ContextsCode0
Investigation on domain adaptation of additive manufacturing monitoring systems to enhance digital twin reusability—0
LARE: Latent Augmentation using Regional Embedding with Vision-Language Model—0
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
Unsupervised Domain Adaptation Via Data Pruning—0
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal ConsistencyCode0
Multimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification—0
Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep UnrollingCode0
Beyond LoRA: Exploring Efficient Fine-Tuning Techniques for Time Series Foundational Models—0
Few-Shot Domain Adaptation for Learned Image Compression—0
Partial Distribution Matching via Partial Wasserstein Adversarial Networks—0
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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