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A comprehensive survey of continual learning: theory, method and application
To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …
Revisiting class-incremental learning with pre-trained models: Generalizability and adaptivity are all you need
Class-incremental learning (CIL) aims to adapt to emerging new classes without forgetting
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
old ones. Traditional CIL models are trained from scratch to continually acquire knowledge …
Three types of incremental learning
Incrementally learning new information from a non-stationary stream of data, referred to as
'continual learning', is a key feature of natural intelligence, but a challenging problem for …
'continual learning', is a key feature of natural intelligence, but a challenging problem for …
Class-incremental learning: A survey
Deep models, eg, CNNs and Vision Transformers, have achieved impressive achievements
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
in many vision tasks in the closed world. However, novel classes emerge from time to time in …
Class-incremental learning: survey and performance evaluation on image classification
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; …
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …
Online continual learning in image classification: An empirical survey
Online continual learning for image classification studies the problem of learning to classify
images from an online stream of data and tasks, where tasks may include new classes …
images from an online stream of data and tasks, where tasks may include new classes …
Continual detection transformer for incremental object detection
Incremental object detection (IOD) aims to train an object detector in phases, each with
annotations for new object categories. As other incremental settings, IOD is subject to …
annotations for new object categories. As other incremental settings, IOD is subject to …
Open-world machine learning: A review and new outlooks
Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …
existing studies are largely based on the closed-world assumption, which assumes that the …
Memory replay with data compression for continual learning
Continual learning needs to overcome catastrophic forgetting of the past. Memory replay of
representative old training samples has been shown as an effective solution, and achieves …
representative old training samples has been shown as an effective solution, and achieves …
Training networks in null space of feature covariance for continual learning
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 …
from catastrophic forgetting. To balance plasticity and stability of network in continual …