Adaptive Feature and Cross-Attention for Domain Generalization in Medical Image Segmentation
This research proposes a novel method for improving the generalization ability of medical image segmentation models across different domains by introducing Adaptive Feature Blending (AFB) for data augmentation and Dual Cross-Attention Regularization (DCAR) for learning domain-invariant representations.
도메인 간극 해소를 위한 의미론적 분할에서의 Diffusion Feature 활용
사전 학습된 텍스트-이미지 Diffusion 모델의 잠재된 지식을 활용하여 도메인 일반화(DG) 의미론적 분할 작업에서 우수한 성능을 달성하는 새로운 방법론을 제시합니다.
CLIP-Based Transfer Learning for Domain Generalized Dense Perception: A Simple Yet Effective Baseline
Fine-tuning vision-language pre-trained models like CLIP offers a surprisingly effective and simple baseline for domain generalization in computer vision tasks, achieving competitive or superior performance to more complex methods in semantic segmentation and object detection.
Causal Predictors Fail to Generalize Better to New Domains in Tabular Datasets
Contrary to theoretical expectations, machine learning models trained on causal features do not demonstrate better generalization across domains compared to models trained on all available features, even when using state-of-the-art causal machine learning methods.
Learning from Multiple Experts (LFME): A Novel Domain Generalization Framework for Enhanced Deep Learning Model Performance
The LFME framework improves the performance of deep learning models in domain generalization by training multiple expert models on different source domains and using their knowledge to guide a universal target model, enabling it to excel across all domains.
Combating Frequency Shortcut Learning in Domain Generalization Using Adversarial Frequency Augmentation
This research paper introduces a novel approach to address the issue of frequency shortcut learning in domain generalization by dynamically manipulating the frequency characteristics of training data using adversarial augmentation techniques.
START: Saliency-Driven Token-Aware Transformation을 사용한 일반화된 State Space Model
본 논문에서는 입력 종속 행렬 내의 도메인 특정 특징이 모델 일반화를 저해할 수 있다는 것을 이론적으로 분석하고, 이를 해결하기 위해 Saliency-Driven Token-Aware Transformation(START)을 제안하여 도메인 일반화 성능을 향상시킵니다.
START: A Saliency-Driven State Space Model for Domain Generalization
START, a novel state space model architecture, enhances domain generalization by using saliency-driven token-aware transformation to mitigate the accumulation of domain-specific features in input-dependent matrices.
잊혀진 영역 일반화를 찾아서: 대규모 데이터셋 시대에서 진정한 OOD 성능 평가
대규모 웹 데이터셋으로 훈련된 CLIP 모델의 뛰어난 성능은 훈련 데이터에 포함된 광범위한 도메인의 이미지 때문이며, 이는 모델이 실제로 OOD 일반화 능력을 갖췄다기보다는 훈련 데이터의 다양성에 의존한다는 것을 의미한다.
인과적 불변 베이지안 신경망을 이용한 강력한 도메인 일반화
본 논문에서는 딥러닝 모델의 도메인 일반화 능력을 향상시키기 위해 인과적 추론과 베이지안 신경망을 결합한 새로운 접근 방식을 제안합니다.
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