Modern artificial intelligence systems require class-incremental learning while suffering from
catastrophic forgetting in many real-world applications. Due to the missing knowledge of …

Most existing 3D object classification and retrieval algorithms rely on one-off supervised
learning on closed 3D object sets and tend to provide rigid convolutional neural networks …

Not forgetting old class knowledge is a key challenge for class-incremental learning (CIL)
when the model continuously adapts to new classes. A common technique to address this is …

Forgetting refers to the loss or deterioration of previously acquired information or knowledge.
While the existing surveys on forgetting have primarily focused on continual learning …

Deep neural networks (DNNs) are prone to the notorious catastrophic forgetting problem
when learning new tasks incrementally. Class-incremental learning (CIL) is a promising …

Lifelong learning aims to train a highly expressive model for a new task while retaining all
knowledge for previous tasks. However, many practical scenarios do not always require the …

Continual learning in real-world scenarios is a major challenge. A general continual
learning model should have a constant memory size and no predefined task boundaries, as …

As the categories of named entities rapidly increase, the deployed NER models are required
to keep updating toward recognizing more entity types, creating a demand for class …

Generative retrieval (GR) directly predicts the identifiers of relevant documents (ie, docids)
based on a parametric model. It has achieved solid performance on many ad-hoc retrieval …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …