Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

The ability of artificial agents to increment their capabilities when confronted with new data is
an open challenge in artificial intelligence. The main challenge faced in such cases is …

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 …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

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 …

Deep learning approaches are nowadays ubiquitously used to tackle computer vision tasks
such as semantic segmentation, requiring large datasets and substantial computational …

Deep learning architectures exhibit a critical drop of performance due to catastrophic
forgetting when they are required to incrementally learn new tasks. Contemporary …

Attributes of sound inherent to objects can provide valuable cues to learn rich
representations for object detection and tracking. Furthermore, the co-occurrence of …

Overcoming catastrophic forgetting in neural networks is a long-standing and core research
objective for incremental learning. Notable studies have shown regularization strategies …

The success of deep learning is usually accompanied by the growth in neural network
depth. However, the traditional training method only supervises the neural network at its last …