Recent self-supervised learning methods are able to learn high-quality image
representations and are closing the gap with supervised approaches. However, these …

Continuous unsupervised representation learning (CURL) research has greatly benefited
from improvements in self-supervised learning (SSL) techniques. As a result, existing CURL …

Abstract In Class Incremental Learning (CIL), a classification model is progressively trained
at each incremental step on an evolving dataset of new classes, while at the same time, it is …

This paper focuses on the prevalent stage interference and stage performance imbalance of
incremental learning. To avoid obvious stage learning bottlenecks, we propose a new …

Continual Learning (CL) involves training a machine learning model in a sequential manner
to learn new information while retaining previously learned tasks without the presence of …

The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class
incremental learning (CIL) by preserving limited exemplars from previous tasks. With …

Lifelong person re-identification (LReID) is in significant demand for real-world development
as a large amount of ReID data is captured from diverse locations over time and cannot be …

Federated learning (FL) is a hot collaborative training framework via aggregating model
parameters of decentralized local clients. However, most FL methods unreasonably assume …

The goal of continual learning is to find a model that solves multiple learning tasks which are
presented sequentially to the learner. A key challenge in this setting is that the learner may" …

Continual learning for segmentation has recently seen increasing interest. However, all
previous works focus on narrow semantic segmentation and disregard panoptic …