Continual Learning for Food Recognition Using Class Incremental Extreme and Online Clustering Method: Self-Organizing Incremental Neural Network

Bansiwala, Rashida; Gosavi, Pramod; Gaikwad, Rahul

doi:10.46335/ijies.2021.6.10.7

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, incremental learning has been explored in different domains, including image classification (Meng et al, 2022;Nguyen et al, 2022;Zhao et al, 2022), natural language processing (Jan Moolman Buys University College University of Oxford, 2017; Kahardipraja et al, 2023), recommender systems (Ouyang et al, 2021;Wang et al, 2021;Ahrabian et al, 2021a), and data stream mining (Eisa et al, 2022). Researchers have investigated different strategies such as incremental decision trees (Barddal and Fabr'ıcio Enembreck., 2020;Choyon et al, 2020;Han et al, 2023), online clustering (Bansiwala et al, 2021), ensemble methods (Lovinger and Valova, 2020;Zhang J. et al, 2023), and deep learning approaches to tackle incremental learning problems (Ali et al, 2022). Incremental learning enables lifelong learning to constantly learn new data new data while leveraging prior knowledge that continues to be an active research topic (Figure 1).…”

Section: Lifelong Learningmentioning

confidence: 99%

Advancing autonomy through lifelong learning: a survey of autonomous intelligent systems

Zhu,

Bu,

Zhu

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

The combination of lifelong learning algorithms with autonomous intelligent systems (AIS) is gaining popularity due to its ability to enhance AIS performance, but the existing summaries in related fields are insufficient. Therefore, it is necessary to systematically analyze the research on lifelong learning algorithms with autonomous intelligent systems, aiming to gain a better understanding of the current progress in this field. This paper presents a thorough review and analysis of the relevant work on the integration of lifelong learning algorithms and autonomous intelligent systems. Specifically, we investigate the diverse applications of lifelong learning algorithms in AIS’s domains such as autonomous driving, anomaly detection, robots, and emergency management, while assessing their impact on enhancing AIS performance and reliability. The challenging problems encountered in lifelong learning for AIS are summarized based on a profound understanding in literature review. The advanced and innovative development of lifelong learning algorithms for autonomous intelligent systems are discussed for offering valuable insights and guidance to researchers in this rapidly evolving field.

show abstract