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Domain Generalization
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The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Distribution-aware Noisy-label Crack Segmentation
Diverse Deep Feature Ensemble Learning for Omni-Domain Generalized Person Re-identification
Aligned Divergent Pathways for Omni-Domain Generalized Person Re-Identification
On the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models
In Search of Forgotten Domain Generalization
Adaptive High-Frequency Transformer for Diverse Wildlife Re-Identification
Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing
QT-DoG: Quantization-aware Training for Domain Generalization
Fine-Tuning CLIP's Last Visual Projector: A Few-Shot Cornucopia
DiffusionFake: Enhancing Generalization in Deepfake Detection via Guided Stable Diffusion
Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classification
FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator
Parameter Competition Balancing for Model Merging
Frequency-Based Federated Domain Generalization for Polyp Segmentation
Identifying Knowledge Editing Types in Large Language Models
Crafting Distribution Shifts for Validation and Training in Single Source Domain Generalization
Exploring Language Model Generalization in Low-Resource Extractive QA
From One to the Power of Many: Invariance to Multi-LiDAR Perception from Single-Sensor Datasets
Advancing Open-Set Domain Generalization Using Evidential Bi-Level Hardest Domain Scheduler
Exploring Acoustic Similarity in Emotional Speech and Music via Self-Supervised Representations
InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography Prediction
AXCEL: Automated eXplainable Consistency Evaluation using LLMs
Benchmarking Domain Generalization Algorithms in Computational Pathology
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization
Soft Segmented Randomization: Enhancing Domain Generalization in SAR-ATR for Synthetic-to-Measured
PromptTA: Prompt-driven Text Adapter for Source-free Domain Generalization
Boosting Federated Domain Generalization: Understanding the Role of Advanced Pre-Trained Architectures
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
Cross-Task Pretraining for Cross-Organ Cross-Scanner Adenocarcinoma Segmentation
Domain Generalization for Endoscopic Image Segmentation by Disentangling Style-Content Information and SuperPixel Consistency
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
Mixture of Prompt Learning for Vision Language Models
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency
In-Context Learning of Linear Systems: Generalization Theory and Applications to Operator Learning
Integrating Audio Narrations to Strengthen Domain Generalization in Multimodal First-Person Action Recognition
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