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 53015350 of 6439 papers

TitleStatusHype
DADA: Depth-aware Domain Adaptation in Semantic SegmentationCode0
Looking back at Labels: A Class based Domain Adaptation TechniqueCode0
Easy Transfer Learning By Exploiting Intra-domain Structures0
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification0
A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging0
Domain Adaptation for Low-Resource Neural Semantic Parsing0
Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks0
Learning to Transfer Examples for Partial Domain AdaptationCode1
Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration0
Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods0
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification0
All about Structure: Adapting Structural Information across Domains for Boosting Semantic SegmentationCode0
Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer0
RecSys-DAN: Discriminative Adversarial Networks for Cross-Domain Recommender Systems0
Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning0
Enhanced Transfer Learning with ImageNet Trained Classification Layer0
Unifying Unsupervised Domain Adaptation and Zero-Shot Visual RecognitionCode0
Manifold Criterion Guided Transfer Learning via Intermediate Domain GenerationCode0
Unconstrained Facial Action Unit Detection via Latent Feature DomainCode0
Cluster Alignment with a Teacher for Unsupervised Domain AdaptationCode0
Regularized Learning for Domain Adaptation under Label ShiftsCode0
Few-shot Adaptive Faster R-CNN0
Multi-Domain Adversarial LearningCode0
Cross Domain Knowledge Transfer for Unsupervised Vehicle Re-identification0
AdaGraph: Unifying Predictive and Continuous Domain Adaptation through GraphsCode1
Domain adaptation for holistic skin detection0
On Target Shift in Adversarial Domain Adaptation0
Zero-shot Domain Adaptation Based on Attribute Information0
Learning Condensed and Aligned Features for Unsupervised Domain Adaptation Using Label Propagation0
Sliced Wasserstein Discrepancy for Unsupervised Domain AdaptationCode0
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework0
Learning from Synthetic Data for Crowd Counting in the Wild0
Support and Invertibility in Domain-Invariant Representations0
Unsupervised Domain Adaptation using Feature-Whitening and Consensus LossCode0
Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment0
Unsupervised Domain Adaptation Learning Algorithm for RGB-D Staircase Recognition0
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware PerspectiveCode0
Unsupervised Tracklet Person Re-IdentificationCode0
Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression0
Non-Parametric Adaptation for Neural Machine Translation0
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion MiningCode0
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural NetworksCode0
Channel adversarial training for cross-channel text-independent speaker recognition0
Joint Multi-Domain Learning for Automatic Short Answer Grading0
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process ApproachCode0
Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation0
Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks0
Learning to see across Domains and Modalities0
Cost-Effective Incentive Allocation via Structured Counterfactual Inference0
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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