Results 81 to 90 of about 842,651 (374)
Class incremental learning via feature space calibration
Computational Visual MediaClass incremental learning (CIL) has attracted a great deal of attention as an effective way to realize lifelong learning. However, existing works still face catastrophic forgetting, i.e., performance degradation on old tasks after learning new category ...
Jeonghoon Kim +4 more
doaj +1 more source
Research on a class-incremental learning method based on sonar images
Xibei Gongye Daxue Xuebao, 2023 Due to the low resolution and the small number of samples of sonar images, the existing class incremental learning networks have a serious problem of catastrophic forgetting of historical task targets, resulting in a low average recognition rate of all ...
CHEN Xinzhe, LIANG Hong, XU Weiyu
doaj +1 more source
A Novel Progressive Multi-label Classifier for Classincremental Data
, 2016 In this paper, a progressive learning algorithm for multi-label classification to learn new labels while retaining the knowledge of previous labels is designed.
Dave, Mihika +3 more
core +1 more source
Gradient Reweighting: Towards Imbalanced Class-Incremental Learning [PDF]
Computer Vision and Pattern RecognitionClass-Incremental Learning (CIL) trains a model to continually recognize new classes from non-stationary data while retaining learned knowledge. A major challenge of CIL arises when applying to real-world data characterized by non-uniform distribution ...
Jiangpeng He, F. Zhu
semanticscholar +1 more source
Federated Class-Incremental Learning with Prompting
Expert Systems with ApplicationsAs Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data privacy when let models learn from data which is distributed across various clients.
Jiale Liu +6 more
openaire +2 more sources
Online Hyperparameter Optimization for Class-Incremental Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge.
Yaoyao Liu 0001 +3 more
openaire +3 more sources
Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Future-proofing class-incremental learning
Machine Vision and ApplicationsExemplar-Free Class Incremental Learning is a highly challenging setting where replay memory is unavailable. Methods relying on frozen feature extractors have drawn attention recently in this setting due to their impressive performances and lower computational costs.
Quentin Jodelet +3 more
openaire +2 more sources
Clinical, histological, and serological predictors of renal function loss in lupus nephritis.
Arthritis Care &Research, Accepted Article.Objective Kidney survival is the ultimate goal in lupus nephritis (LN) management, but long‐term predictors remain inadequately studied, requiring long‐term follow‐up. This study aimed to identify baseline and early longitudinal predictors of kidney survival in the Accelerating Medicines Partnership LN longitudinal cohort.
Shangzhu Zhang +21 more
wiley +1 more source
Opportunistic Dynamic Architecture for Class-Incremental Learning
IEEE AccessContinual learning has attracted increasing attention over the last few years, as it enables to continually learn new tasks over time, which has significant implication to many real-world applications.
Fahrurrozi Rahman +2 more
doaj +1 more source