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 39514000 of 6439 papers

TitleStatusHype
Weak Adaptation Learning: Addressing Cross-Domain Data Insufficiency With Weak Annotator0
On L_2-consistency of nearest neighbor matching0
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes0
Weakly supervised covariance matrices alignment through Stiefel matrices estimation for MEG applications0
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation0
Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains0
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation0
Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects0
Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer0
Weakly Supervised Object Localization With Progressive Domain Adaptation0
Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration0
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation0
Weather-Aware Object Detection Transformer for Domain Adaptation0
WebVision Database: Visual Learning and Understanding from Web Data0
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis0
WeightAlign: Normalizing Activations by Weight Alignment0
Weight Averaging for Out-of-Distribution Generalization and Few-Shot Domain Adaptation0
Weight-based Mask for Domain Adaptation0
Weighted Joint Maximum Mean Discrepancy Enabled Multi-Source-Multi-Target Unsupervised Domain Adaptation Fault Diagnosis0
Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data0
Training BERT Models to Carry Over a Coding System Developed on One Corpus to Another0
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation0
What can we learn about CNNs from a large scale controlled object dataset?0
What My Motion tells me about Your Pose: A Self-Supervised Monocular 3D Vehicle Detector0
What's in a Domain? Analyzing Genre and Topic Differences in Statistical Machine Translation0
What to do about bad language on the internet0
Emotion analysis and detection during COVID-190
When Annotation Schemes Change Rules Help: A Configurable Approach to Coreference Resolution beyond OntoNotes0
When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach0
When Giant Language Brains Just Aren't Enough! Domain Pizzazz with Knowledge Sparkle Dust0
When Naive Bayes Nearest Neighbors Meet Convolutional Neural Networks0
When Naïve Bayes Nearest Neighbours Meet Convolutional Neural Networks0
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation0
When Source-Free Domain Adaptation Meets Learning with Noisy Labels0
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity0
Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images0
Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound0
Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution0
Word-based Domain Adaptation for Neural Machine Translation0
Word-Context Character Embeddings for Chinese Word Segmentation0
Word Segmentation of Informal Arabic with Domain Adaptation0
WUT at SemEval-2019 Task 9: Domain-Adversarial Neural Networks for Domain Adaptation in Suggestion Mining0
Xiaomi's Submissions for IWSLT 2020 Open Domain Translation Task0
XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model0
XQA-DST: Multi-Domain and Multi-Lingual Dialogue State Tracking0
X-Sim: Cross-Embodiment Learning via Real-to-Sim-to-Real0
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets0
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing0
YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries0
YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models -0
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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
4PMTransAccuracy89Unverified
5CMKDAccuracy89Unverified
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
8MEDMAccuracy88.9Unverified
9DDAAccuracy88.9Unverified
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