Incremental learning has become a new research hotspot in the field of machine learning.
Compared with traditional machine learning, incremental learning can continuously learn …

This paper focuses on the prevalent stage interference and stage performance imbalance of
incremental learning. To avoid obvious stage learning bottlenecks, we propose a new …

The influence of class orderings in the evaluation of incremental learning has received very
little attention. In this paper, we investigate the impact of class orderings for incrementally …

Incremental learning suffers from two challenging problems; forgetting of old knowledge and
intransigence on learning new knowledge. Prediction by the model incrementally learned …

Artificial neural networks have exceeded human-level performance in accomplishing
several individual tasks (eg voice recognition, object recognition, and video games) …

Incremental Learning (IL) is useful when artificial systems need to deal with streams of data
and do not have access to all data at all times. The most challenging setting requires a …

Despite the impressive performance in many individual tasks, deep neural networks suffer
from catastrophic forgetting when learning new tasks incrementally. Recently, various …

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 …

Modern deep learning approaches have achieved great success in many vision applications
by training a model using all available task-specific data. However, there are two major …

Class-incremental learning is becoming more popular as it helps models widen their
applicability while not forgetting what they already know. A trend in this area is to use a …