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 …

We discuss a general formulation for the Continual Learning (CL) problem for classification—
a learning task where a stream provides samples to a learner and the goal of the learner …

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

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 …

Continual Learning (CL) aims to sequentially train models on streams of incoming data that
vary in distribution by preserving previous knowledge while adapting to new data. Current …

Deep-learning-based methods are widely used in multisource remote-sensing image
classification, and the improvement in their performance confirms the effectiveness of deep …

Abstract Class-Incremental Learning (CIL) aims to learn a classification model with the
number of classes increasing phase-by-phase. An inherent problem in CIL is the stability …

Real-world applications require the classification model to adapt to new classes without
forgetting old ones. Correspondingly, Class-Incremental Learning (CIL) aims to train a …

Exemplar-free class-incremental learning is very challenging due to the negative effect of
catastrophic forgetting. A balance between stability and plasticity of the incremental process …

Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts
gradually, where only a few examples per concept are available to the learner. Due to the …