Dynamical system response under Gaussian and Poisson white noises solved by deep neural network method with adaptive task decomposition and progressive learning strategy [0.03%]
自适应任务分解和渐进学习策略的深度神经网络方法及其求解高斯白噪声与泊松白噪声下的动力系统响应问题
Wantao Jia,Xiaotong Feng,Yifan Zhao et al.
Wantao Jia et al.
The forward Kolmogorov equation corresponding to a system under the combined excitation of Gaussian and Poisson white noises is an integrodifferential equation (IDE). In our recent study, we introduced GL-PINNs, which integrates the Gauss-L...
Enhancing 18F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm [0.03%]
利用深度渐进学习重建算法改善不同体质指数下的18F-FDG PET图像质量和病灶诊断性能
Zhihao Chen,Hongxing Yang,Ming Qi et al.
Zhihao Chen et al.
Background: As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) ...
Bias to Balance: New-Knowledge-Preferred Few-Shot Class-Incremental Learning via Transition Calibration [0.03%]
从偏差到平衡：通过转换校准的新型知识优先的少量样本类渐进学习
Hongquan Zhang,Zhizhong Zhang,Xin Tan et al.
Hongquan Zhang et al.
Humans can quickly learn new concepts with limited experience, while not forgetting learned knowledge. Such ability in machine learning is referred to as few-shot class-incremental learning (FSCIL). Although some methods try to solve this p...
Full-dimensional dynamic convolution and progressive learning strategy for strawberry recognition based on YOLOv8 [0.03%]
基于YOLOv8的全维度动态卷积和渐进学习策略的草莓识别方法
Liping Bai,Chenglei Xia,Fei Liu et al.
Liping Bai et al.
The growth of strawberries is influenced by environmental diversity and spatial dispersion, which present significant challenges for accurate identification and real-time image processing in complex environments. This paper addresses these ...
A prompt regularization approach to enhance few-shot class-incremental learning with Two-Stage Classifier [0.03%]
一种促进少量样本类别渐进学习的提示正则化方法及其两阶段分类器
Meilan Hao,Yizhan Gu,Kejian Dong et al.
Meilan Hao et al.
With a limited number of labeled samples, Few-Shot Class-Incremental Learning (FSCIL) seeks to efficiently train and update models without forgetting previously learned tasks. Because pre-trained models can learn extensive feature represent...
Automatic cassava disease recognition using object segmentation and progressive learning [0.03%]
使用对象分割和增量学习进行自动木薯病害识别
Chang Che,Nian Xue,Zhen Li et al.
Chang Che et al.
Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environ...
Kai Hu,Yunjiang Wang,Yuan Zhang et al.
Kai Hu et al.
The goal of few-shot class incremental learning (FSCIL) is to learn new concepts from a limited number of novel samples while preserving the knowledge of previously learned classes. The mainstream FSCIL framework begins with training in the...
Real-Time Progressive Learning: Accumulate Knowledge From Control With Neural-Network-Based Selective Memory [0.03%]
基于神经网络的选择性记忆的实时渐进学习：从控制中积累知识
Yiming Fei,Jiangang Li,Yanan Li
Yiming Fei
Memory, as the basis of learning, determines the storage, update, and forgetting of knowledge and further determines the efficiency of learning. Featured with the mechanism of memory, a radial basis function neural network (RBFNN)-based lea...
Improving forward compatibility in class incremental learning by increasing representation rank and feature richness [0.03%]
通过增加表示秩和特征丰富度提高类渐进学习的前向兼容性
Jaeill Kim,Wonseok Lee,Moonjung Eo et al.
Jaeill Kim et al.
Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining knowledge obtained from prior tasks. Although previous studi...
Language-Inspired Relation Transfer for Few-Shot Class-Incremental Learning [0.03%]
基于语言启发关系迁移的少量样本类渐进学习方法
Yifan Zhao,Jia Li,Zeyin Song et al.
Yifan Zhao et al.
Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems. This lifelong learning capability helps to distinguish new knowledge from old ones through the increase of open-world le...
耗时 0.11446 秒，为您在
48223910
条记录里面共找到 78 篇文章 [XML]