Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Abstract Class-Incremental Learning (CIL)[38] trains classifiers under a strict memory
budget: in each incremental phase, learning is done for new data, most of which is …

A large body of research in continual learning is devoted to overcoming the catastrophic
forgetting of neural networks by designing new algorithms that are robust to the distribution …

With the memory-resource-limited constraints, class-incremental learning (CIL) usually
suffers from the “catastrophic forgetting” problem when updating the joint classification …

Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of
new classes but few-shot exemplars of old classes in each incremental phase, where the" …

For a deep learning model, the network architecture is crucial as a model with inappropriate
architecture often suffers from performance degradation or parameter redundancy. However …

Abstract Class Incremental Learning (CIL) is inspired by the human ability to learn new
classes without forgetting previous ones. CIL becomes more challenging in real-world …

Recent class-incremental learning methods combine deep neural architectures and learning
algorithms to handle streaming data under memory and computational constraints. The …

As progress is made on training machine learning models on incrementally expanding
classification tasks (ie, incremental learning), a next step is to translate this progress to …

Despite the significant advances achieved in Artificial Neural Networks (ANNs), their design
process remains notoriously tedious, depending primarily on intuition, experience and trial …