We present a novel class incremental learning approach based on deep neural networks,
which continually learns new tasks with limited memory for storing examples in the previous …

Deep neural networks (DNNs) often suffer from" catastrophic forgetting" during incremental
learning (IL)---an abrupt degradation of performance on the original set of classes when the …

Although deep learning approaches have stood out in recent years due to their state-of-the-
art results, they continue to suffer from catastrophic forgetting, a dramatic decrease in overall …

Deep neural networks (DNNs) have been applied in class incremental learning, which aims
to solve common real-world problems of learning new classes continually. One drawback of …

Modern machine learning suffers from catastrophic forgetting when learning new classes
incrementally. The performance dramatically degrades due to the missing data of old …

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 …

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

Conventionally, deep neural networks are trained offline, relying on a large dataset
prepared in advance. This paradigm is often challenged in real-world applications, eg online …

Deep learning models tend to forget their earlier knowledge while incrementally learning
new tasks. This behavior emerges because the parameter updates optimized for the new …

Deep learning systems typically suffer from catastrophic forgetting of past knowledge when
acquiring new skills continually. In this paper, we emphasize two dilemmas, representation …