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 51515200 of 6439 papers

TitleStatusHype
Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning0
Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach0
Domain Adaptation for CRF-based Chinese Word Segmentation using Free Annotations0
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games0
Domain Adaptation for Dense Retrieval and Conversational Dense Retrieval through Self-Supervision by Meticulous Pseudo-Relevance Labeling0
Domain Adaptation for Dense Retrieval through Self-Supervision by Pseudo-Relevance Labeling0
Domain Adaptation for Dependency Parsing via Self-Training0
Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images0
Domain Adaptation for Disease Phrase Matching with Adversarial Networks0
Domain Adaptation for Efficiently Fine-tuning Vision Transformer with Encrypted Images0
Domain Adaptation for Enterprise Email Search0
Domain Adaptation for Facial Expression Classifier via Domain Discrimination and Gradient Reversal0
Domain Adaptation For Formant Estimation Using Deep Learning0
Domain Adaptation for Hindi-Telugu Machine Translation Using Domain Specific Back Translation0
Domain adaptation for holistic skin detection0
Domain Adaptation for Industrial Time-series Forecasting via Counterfactual Inference0
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey0
Domain Adaptation for Infection Prediction from Symptoms Based on Data from Different Study Designs and Contexts0
Domain Adaptation for Large-Vocabulary Object Detectors0
Domain Adaptation for Learning Generator from Paired Few-Shot Data0
Domain Adaptation for Low-Resource Neural Semantic Parsing0
Domain Adaptation for Medical Image Analysis: A Survey0
Domain Adaptation for Medical Text Translation using Web Resources0
Domain Adaptation for MT: A Study with Unknown and Out-of-Domain Tasks0
Domain Adaptation for Named Entity Recognition in Online Media with Word Embeddings0
Domain Adaptation for Named Entity Recognition Using CRFs0
Domain Adaptation for Neural Machine Translation0
Domain Adaptation for Neural Networks by Parameter Augmentation0
Domain Adaptation for NMT via Filtered Iterative Back-Translation0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples0
Domain Adaptation for Outdoor Robot Traversability Estimation from RGB data with Safety-Preserving Loss0
Domain Adaptation for Parsing0
Domain adaptation for part-of-speech tagging of noisy user-generated text0
Domain Adaptation for Person-Job Fit with Transferable Deep Global Match Network0
Domain Adaptation for Rare Classes Augmented with Synthetic Samples0
Domain Adaptation for Real-World Single View 3D Reconstruction0
Domain Adaptation for Reinforcement Learning on the Atari0
Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network0
Domain Adaptation for Resume Classification Using Convolutional Neural Networks0
Domain Adaptation for Robust Workload Level Alignment Between Sessions and Subjects using fNIRS0
Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection0
Domain Adaptation for Semantic Parsing0
Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning0
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation0
Domain Adaptation for Sentiment Analysis using Keywords in the Target Domain as the Learning Weight0
Domain adaptation for sequence labeling using hidden Markov models0
Domain Adaptation for Sparse-Data Settings: What Do We Gain by Not Using Bert?0
Domain Adaptation for Statistical Machine Translation0
Domain Adaptation for Structured Output via Disentangled Patch Representations0
Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FFTATAverage Accuracy96Unverified
2PMTransAverage Accuracy95.3Unverified
3CMKDAverage Accuracy94.4Unverified
4SSRT-B (ours)Average Accuracy93.5Unverified
5CDTransAverage Accuracy92.6Unverified
6CoViAverage Accuracy91.8Unverified
7GSDEAverage Accuracy91.7Unverified
8FixBiAverage Accuracy91.4Unverified
9Contrastive Adaptation NetworkAverage Accuracy90.6Unverified
10BIWAAAverage Accuracy90.5Unverified
#ModelMetricClaimedVerifiedStatus
1HALOmIoU78.1Unverified
2ILM-ASSLmIoU76.6Unverified
3DCFmIoU69.3Unverified
4HRDA+PiPamIoU68.2Unverified
5MICmIoU67.3Unverified
6FREDOM - TransformermIoU67Unverified
7HRDAmIoU65.8Unverified
8SePiComIoU64.3Unverified
9MIC + Guidance TrainingmIoU63.8Unverified
10DAFormer + ProCSTmIoU61.6Unverified
#ModelMetricClaimedVerifiedStatus
1HALOmIoU77.8Unverified
2DCFmIoU77.7Unverified
3ILM-ASSLmIoU76.1Unverified
4MICmIoU75.9Unverified
5HRDA+PiPamIoU75.6Unverified
6HRDAmIoU73.8Unverified
7FREDOM - TransformermIoU73.6Unverified
8HALOmIoU73.3Unverified
9SePiComIoU70.3Unverified
10DAFormer + ProCSTmIoU69.4Unverified
#ModelMetricClaimedVerifiedStatus
1SWGAccuracy92.3Unverified
2RCLAccuracy90Unverified
3PGA (ViT-L/14)Accuracy89.4Unverified
4CMKDAccuracy89Unverified
5PMTransAccuracy89Unverified
6MICAccuracy86.2Unverified
7PGA (ViT-B/16)Accuracy85.1Unverified
8ELSAccuracy84.6Unverified
9SDAT (ViT-B/16)Accuracy84.3Unverified
10CDTrans (DeiT-B)Accuracy80.5Unverified
#ModelMetricClaimedVerifiedStatus
1FFTATAccuracy93.8Unverified
2RCLAccuracy93.2Unverified
3MICAccuracy92.8Unverified
4SWGAccuracy92.7Unverified
5CMKDAccuracy91.8Unverified
6DePTAccuracy90.7Unverified
7SDAT(ViT)Accuracy89.8Unverified
8SFDA2++Accuracy89.6Unverified
9PMtransAccuracy88.8Unverified
10CoViAccuracy88.5Unverified
#ModelMetricClaimedVerifiedStatus
1CMKDAccuracy94.3Unverified
2MCC+NWDAccuracy90.7Unverified
3GLOT-DRAccuracy90.4Unverified
4SPLAccuracy90.3Unverified
5DFA-SAFNAccuracy90.2Unverified
6DADAAccuracy89.3Unverified
7DFA-ENTAccuracy89.1Unverified
8DDAAccuracy88.9Unverified
9MEDMAccuracy88.9Unverified
10IAFN+ENTAccuracy88.9Unverified
#ModelMetricClaimedVerifiedStatus
1SoRAmIoU78.8Unverified
2ReinmIoU77.6Unverified
3CoDAmIoU72.6Unverified
4Refign (HRDA)mIoU72.1Unverified
5HALOmIoU71.9Unverified
6MICmIoU70.4Unverified
7HRDAmIoU68Unverified
8Refign (DAFormer)mIoU65.5Unverified
9VBLC (DAFormer)mIoU64.2Unverified
10CMFormermIoU60.1Unverified
#ModelMetricClaimedVerifiedStatus
1FACTAccuracy98.8Unverified
2FAMCDAccuracy98.72Unverified
3DFA-MCDAccuracy98.6Unverified
4Mean teacherAccuracy98.26Unverified
5DRANetAccuracy98.2Unverified
6SHOTAccuracy98Unverified
7DFA-ENTAccuracy97.9Unverified
8CyCleGAN (Light-weight Calibrator)Accuracy97.1Unverified