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 …

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 …

Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a
model forgets previously learned information while acquiring new knowledge. As large …

We introduce MIPS-Fusion, a robust and scalable online RGB-D reconstruction method
based on a novel neural implicit representation-multi-implicit-submap. Different from existing …

Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart
the process over again once new data becomes available. A much cheaper and more …

Online continual learning aims to get closer to a live learning experience by learning directly
on a stream of data with temporally shifting distribution and by storing a minimum amount of …

Pre-trained models are commonly used in Continual Learning to initialize the model before
training on the stream of non-stationary data. However, pre-training is rarely applied during …

Abstract In Class-Incremental Learning (CIL) an image classification system is exposed to
new classes in each learning session and must be updated incrementally. Methods …

In Continual learning (CL) balancing effective adaptation while combating catastrophic
forgetting is a central challenge. Many of the recent best-performing methods utilize various …

Although a number of studies are devoted to novel category discovery, most of them assume
a static setting where both labeled and unlabeled data are given at once for finding new …