Exemplar-free class-incremental learning using a backbone trained from scratch and
starting from a small first task presents a significant challenge for continual representation …

Abstract Generalized Continual Category Discovery (GCCD) tackles learning from
sequentially arriving, partially labeled datasets while uncovering new categories. Traditional …

Abstract Continual Novel Class Discovery (CNCD) aims to adapt a trained classification
model to a new task while maintaining its performance on the old task. This presents two …

Humans are excellent at adapting to constantly changing circumstances, but deep neural
networks have catastrophic forgetting. Recently, significant progress has been made with …

We formulate a unifying framework for unsupervised continual learning (UCL), which
disentangles learning objectives that are specific to the present and the past data …

Unsupervised continual learning (UCL) has made remarkable progress over the past two
years, significantly expanding the application of continual learning (CL). However, existing …

This study investigates the loss of generalization ability in neural networks, revisiting warm-
starting experiments from Ash & Adams. Our empirical analysis reveals that common …

Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general
representations from vast amounts of unlabeled data. However, as real-world applications …

Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing
on enabling neural networks to sequentially learn tasks without explicit label information …

Recent research on Self-Supervised Learning (SSL) has demonstrated its ability to extract
high-quality representations from unlabeled samples. However, in continual learning …