Deep network architectures struggle to continually learn new tasks without forgetting the
previous tasks. A recent trend indicates that dynamic architectures based on an expansion …

Deep neural networks achieve state-of-the-art and sometimes super-human performance
across a variety of domains. However, when learning tasks sequentially, the networks easily …

Continual learning is a setting where machine learning models learn novel concepts from
continuously shifting training data, while simultaneously avoiding degradation of knowledge …

Continual learning requires incremental compatibility with a sequence of tasks. However,
the design of model architecture remains an open question: In general, learning all tasks …

In contrast to the natural capabilities of humans to learn new tasks in a sequential fashion,
neural networks are known to suffer from catastrophic forgetting, where the model's …

Continual learning has been widely studied in recent years to resolve the catastrophic
forgetting of deep neural networks. In this paper, we first enforce a low-rank filter subspace …

Continual learning methods aim at training a neural network from sequential data with
streaming labels, relieving catastrophic forgetting. However, existing methods are based on …

The mainstream paradigm behind continual learning has been to adapt the model
parameters to non-stationary data distributions, where catastrophic forgetting is the central …

In the setting of continual learning, a network is trained on a sequence of tasks, and suffers
from catastrophic forgetting. To balance plasticity and stability of network in continual …

The goal of continual learning is to improve the performance of recognition models in
learning sequentially arrived data. Although most existing works are established on the …