A Semi-Supervised Incremental Learning Framework for Sports Video View Classification

2006 ◽  
Author(s):  
Jun Wu ◽  
Bo Zhang ◽  
Xian-Sheng Hua ◽  
Jianwei Zhang
Keyword(s):  
Incremental Learning ◽  
Sports Video ◽  
Learning Framework

scholarly journals Incremental Learning Framework for Mining Big Data Stream

2022 ◽  
Vol 71 (2) ◽  
pp. 2901-2921
Author(s):  
Alaa Eisa ◽  
Nora EL-Rashidy ◽  
Mohammad Dahman Alshehri ◽  
Hazem M. El-bakry ◽  
Samir Abdelrazek
Keyword(s):  
Big Data ◽  
Incremental Learning ◽  
Data Stream ◽  
Learning Framework

scholarly journals Frequency-Temporal Disagreement Adaptation for Robotic Terrain Classification via Vibration in a Dynamic Environment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6550 ◽  
Author(s):  
Chen Cheng ◽  
Ji Chang ◽  
Wenjun Lv ◽  
Yuping Wu ◽  
Kun Li ◽  
...  
Keyword(s):  
Incremental Learning ◽  
Classification Accuracy ◽  
Concept Drift ◽  
Dynamic Environment ◽  
Temporal Correlation ◽  
Classification Methods ◽  
Terrain Classification ◽  
Localization Accuracy ◽  
Learning Framework ◽  
Control Scheme

The accurate terrain classification in real time is of great importance to an autonomous robot working in field, because the robot could avoid non-geometric hazards, adjust control scheme, or improve localization accuracy, with the aid of terrain classification. In this paper, we investigate the vibration-based terrain classification (VTC) in a dynamic environment, and propose a novel learning framework, named DyVTC, which tackles online-collected unlabeled data with concept drift. In the DyVTC framework, the exterior disagreement (ex-disagreement) and interior disagreement (in-disagreement) are proposed novely based on the feature diversity and intrinsic temporal correlation, respectively. Such a disagreement mechanism is utilized to design a pseudo-labeling algorithm, which shows its compelling advantages in extracting key samples and labeling; and consequently, the classification accuracy could be retrieved by incremental learning in a changing environment. Since two sets of features are extracted from frequency and time domain to generate disagreements, we also name the proposed method feature-temporal disagreement adaptation (FTDA). The real-world experiment shows that the proposed DyVTC could reach an accuracy of 89.5%, but the traditional time- and frequency-domain terrain classification methods could only reach 48.8% and 71.5%, respectively, in a dynamic environment.

ILCOC: An Incremental Learning Framework based on Contrastive One-class Classifiers

2021 ◽  
Author(s):  
Wenju Sun ◽  
Jing Zhang ◽  
Danyu Wang ◽  
Yangli-Ao Geng ◽  
Qingyong Li
Keyword(s):  
Incremental Learning ◽  
Learning Framework

scholarly journals Biologically Inspired Sleep Algorithm for Reducing Catastrophic Forgetting in Neural Networks

2020 ◽  
Vol 34 (10) ◽  
pp. 13933-13934
Author(s):  
Timothy Tadros ◽  
Giri Krishnan ◽  
Ramyaa Ramyaa ◽  
Maxim Bazhenov
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Memory Consolidation ◽  
Biological Networks ◽  
Incremental Learning ◽  
Biologically Inspired ◽  
Learning Framework ◽  
Multiple Tasks ◽  
Artificial Neural

Artificial neural networks (ANNs) are known to suffer from catastrophic forgetting: when learning multiple tasks, they perform well on the most recently learned task while failing to perform on previously learned tasks. In biological networks, sleep is known to play a role in memory consolidation and incremental learning. Motivated by the processes that are known to be involved in sleep generation in biological networks, we developed an algorithm that implements a sleep-like phase in ANNs. In an incremental learning framework, we demonstrate that sleep is able to recover older tasks that were otherwise forgotten. We show that sleep creates unique representations of each class of inputs and neurons that were relevant to previous tasks fire during sleep, simulating replay of previously learned memories.

Sign in / Sign up

Export Citation Format

Share Document