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Domain Adaptation
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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.
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<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
A Privacy-Preserving Semantic-Segmentation Method Using Domain-Adaptation Technique
Simulate, Refocus and Ensemble: An Attention-Refocusing Scheme for Domain Generalization
GLAD: Generalizable Tuning for Vision-Language Models
MoTM: Towards a Foundation Model for Time Series Imputation based on Continuous Modeling
InstructFLIP: Exploring Unified Vision-Language Model for Face Anti-spoofing
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
The Bayesian Approach to Continual Learning: An Overview
From Physics to Foundation Models: A Review of AI-Driven Quantitative Remote Sensing Inversion
An Offline Mobile Conversational Agent for Mental Health Support: Learning from Emotional Dialogues and Psychological Texts with Student-Centered Evaluation
Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Integrated Structural Prompt Learning for Vision-Language Models
Prompt-Free Conditional Diffusion for Multi-object Image Augmentation
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries
Bridging Domain Generalization to Multimodal Domain Generalization via Unified Representations
Helping CLIP See Both the Forest and the Trees: A Decomposition and Description Approach
Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
UMDATrack: Unified Multi-Domain Adaptive Tracking Under Adverse Weather Conditions
DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation
Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and Transferability
Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition
Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation
Recognizing Surgical Phases Anywhere: Few-Shot Test-time Adaptation and Task-graph Guided Refinement
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
Exploring Graph-Transformer Out-of-Distribution Generalization Abilities
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools
FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization
Efficient and Generalizable Speaker Diarization via Structured Pruning of Self-Supervised Models
Enhanced Hybrid Transducer and Attention Encoder Decoder with Text Data
Rethinking the Role of Operating Conditions for Learning-based Multi-condition Fault Diagnosis
Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments
Adaptive Control Attention Network for Underwater Acoustic Localization and Domain Adaptation
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