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 40014050 of 6439 papers

TitleStatusHype
You Only Crash Once: Improved Object Detection for Real-Time, Sim-to-Real Hazardous Terrain Detection and Classification for Autonomous Planetary Landings0
You Only Crash Once v2: Perceptually Consistent Strong Features for One-Stage Domain Adaptive Detection of Space Terrain0
Your Classifier can Secretly Suffice Multi-Source Domain Adaptation0
Zero-Annotation Object Detection with Web Knowledge Transfer0
Zero-Forget Preservation of Semantic Communication Alignment in Distributed AI Networks0
Zero-Round Active Learning0
Zero-Shot Adaptive Transfer for Conversational Language Understanding0
Zero-Shot Classification With Discriminative Semantic Representation Learning0
Zero-Shot Deep Domain Adaptation0
Zero-shot Domain Adaptation Based on Attribute Information0
Zero-shot domain adaptation based on dual-level mix and contrast0
Zero-shot Domain Adaptation for Neural Machine Translation with Retrieved Phrase-level Prompts0
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection0
Zero-Shot Domain Adaptation via Kernel Regression on the Grassmannian0
Zero-shot Domain Adaptation without Domain Semantic Descriptors0
Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics0
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering0
Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration0
Zero-Shot Policy Transfer with Disentangled Attention0
Zero-shot prompt-based classification: topic labeling in times of foundation models in German Tweets0
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty0
Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks0
Zero-Shot Semantic Segmentation via Spatial and Multi-Scale Aware Visual Class Embedding0
Zero-Shot Temporal Resolution Domain Adaptation for Spiking Neural Networks0
Zero Shot Time Series Forecasting Using Kolmogorov Arnold Networks0
Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases0
ZIP-FIT: Embedding-Free Data Selection via Compression-Based Alignment0
An Effective Approach to Embedding Source Code by Combining Large Language and Sentence Embedding Models0
Named-Entity Tagging and Domain adaptation for Better Customized Translation0
Deep Learning to Predict Student Outcomes0
Unsupervised Domain Adaptation via Regularized Conditional Alignment0
Consensus Clustering: An Embedding Perspective, Extension and Beyond0
Domain Adaptation with Optimal Transport on the Manifold of SPD matrices0
Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation0
TUNA-Net: Task-oriented UNsupervised Adversarial Network for Disease Recognition in Cross-Domain Chest X-rays0
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation0
1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation0
AstroLLaMA: Towards Specialized Foundation Models in Astronomy0
Prototypical Partial Optimal Transport for Universal Domain Adaptation0
SNFinLLM: Systematic and Nuanced Financial Domain Adaptation of Chinese Large Language Models0
DARTer: Dynamic Adaptive Representation Tracker for Nighttime UAV Tracking0
Robust Emotion Recognition via Bi-Level Self-Supervised Continual Learning0
Bias and Generalizability of Foundation Models across Datasets in Breast Mammography0
A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment0
LegoSLM: Connecting LLM with Speech Encoder using CTC Posteriors0
2nd Place Solution for VisDA 2021 Challenge -- Universally Domain Adaptive Image Recognition0
360SFUDA++: Towards Source-free UDA for Panoramic Segmentation by Learning Reliable Category Prototypes0
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation0
3D Ego-Pose Estimation via Imitation Learning0
3DS: Decomposed Difficulty Data Selection's Case Study on LLM Medical Domain Adaptation0
Show:102550
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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