Results 31 to 40 of about 103,215 (258)
Heterogeneous Domain Generalization Via Domain Mixup [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 One of the main drawbacks of deep Convolutional Neural Networks (DCNN) is that they lack generalization capability. In this work, we focus on the problem of heterogeneous domain generalization which aims to improve the generalization capability across different tasks, which is, how to learn a DCNN model with multiple domain data such that the trained ...
Yufei Wang 0006 +2 more
openaire +2 more sources
Domain generalization by class-aware negative sampling-based contrastive learning
AI Open, 2022 When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy protection.
Mengwei Xie +3 more
doaj +1 more source
Generative Classifier for Domain Generalization
CoRRDomain generalization (DG) aims to improve the generalizability of computer vision models toward distribution shifts. The mainstream DG methods focus on learning domain invariance, however, such methods overlook the potential inherent in domain-specific information.
Shaocong Long +7 more
openaire +2 more sources
Implicit Sharpness-Aware Minimization for Domain Generalization
Remote SensingDomain generalization (DG) aims to learn knowledge from multiple related domains to achieve a robust generalization performance in unseen target domains, which is an effective approach to mitigate domain shift in remote sensing image classification ...
Mingrong Dong +4 more
doaj +1 more source
Federated Domain Generalization with Domain-Specific Soft Prompts Generation
2025 IEEE/CVF International Conference on Computer Vision (ICCV)Prompt learning has become an efficient paradigm for adapting CLIP to downstream tasks. Compared with traditional fine-tuning, prompt learning optimizes a few parameters yet yields highly competitive results, especially appealing in federated learning for computational efficiency. engendering domain shift among clients and posing a formidable challenge
Jianhan Wu 0001 +3 more
openaire +2 more sources
Improving Domain Generalization with Domain Relations
, 2023 Accepted by ICLR 2024 (Spotlight)
Huaxiu Yao +5 more
openaire +3 more sources
Gradient Matching for Domain Generalization
CoRR, 2021 Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their ability to generalize to unseen domains. Here, we propose an inter-domain gradient matching objective that targets domain generalization by maximizing the inner product ...
Shi, Yuge +6 more
openaire +5 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Children with sickle cell disease (SCD) face multiple acute and chronic medical complications that may impact their quality of life as reported by patients themselves. Health‐related social needs (HRSNs), such as food and housing insecurity, are common in people with SCD, but the association between HRSNs and patient‐reported ...
Sarah J. Marks +5 more
wiley +1 more source
Enhancing dynamic domain generalization with weight perturbation training
Journal of King Saud University: Computer and Information SciencesDomain Generalization (DG) seeks to learn models that perform well on unseen target domains. While Dynamic Domain Generalization (DDG) introduces instance-wise adaptability, existing solutions often depend on data-level mixing and explicit domain labels,
Zhishu Sun +5 more
doaj +1 more source
AF-Domains and Their Generalizations [PDF]
Communications in Algebra, 2009 15 ...
openaire +2 more sources