Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

Taking time to identify expected products and waiting for the checkout in a retail store are
common scenes we all encounter in our daily lives. The realization of automatic product …

For future learning systems, incremental learning is desirable because it allows for: efficient
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …

Trojaning attack on neural networks Page 1 Please do not remove this page Trojaning
attack on neural networks Liu, Yingqi; Ma, Shiqing; Aafer, Yousra; et.al. https://scholarship.libraries.rutgers.edu/esploro/outputs/conferencePaper/Trojaning-attack-on-neural-networks/991031794682704646/filesAndLinks …

Continuous/Lifelong learning of high-dimensional data streams is a challenging research
problem. In fact, fully retraining models each time new data become available is infeasible …

Anomaly detection in surveillance videos has been recently gaining attention. A challenging
aspect of high-dimensional applications such as video surveillance is continual learning …

Endoscopic high-speed video (HSV) systems for visualization and assessment of vocal fold
dynamics in the larynx are diverse and technically advancing. To consider resulting …

Near real-time rainfall monitoring at local scale is essential for urban flood risk mitigation.
Previous research on precipitation visual effects supports the idea of vision-based rain …

Online continual learning (OCL) refers to the ability of a system to learn over time from a
continuous stream of data without having to revisit previously encountered training samples …

Annotation quality and quantity positively affect the learning performance of sequence
labeling, a vital task in Natural Language Processing. Hiring domain experts to annotate a …