Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …

Recent years have witnessed an incredibly increasing interest in the topic of incremental
learning. Unlike conventional machine learning situations, data flow targeted by incremental …

Recently, incremental and on-line learning gained more attention especially in the context of
big data and learning from data streams, conflicting with the traditional assumption of …

As a special case of machine learning, incremental learning can acquire useful knowledge
from incoming data continuously while it does not need to access the original data. It is …

Incremental Learning (IL) is an interesting AI problem when the algorithm is assumed to
work on a budget. This is especially true when IL is modeled using a deep learning …

A major open problem on the road to artificial intelligence is the development of
incrementally learning systems that learn about more and more concepts over time from a …

Recent years have witnessed growing interests in developing deep models for incremental
learning. However, existing approaches often utilize the fixed structure and online …

Continual learning, the setting where a learning agent is faced with a never-ending stream
of data, continues to be a great challenge for modern machine learning systems. In …

This work builds upon previous efforts in online incremental learning, namely the
Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data …

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; …