Conventional machine learning studies generally assume close-environment scenarios
where important factors of the learning process hold invariant. With the great success of …

Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Artificial neural networks thrive in solving the classification problem for a particular rigid task,
acquiring knowledge through generalized learning behaviour from a distinct training phase …

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten
by new incoming information while important, frequently used knowledge is prevented from …

Methods proposed in the literature towards continual deep learning typically operate in a
task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all …

In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially
outperforms training from scratch. When using fine-tuning, the underlying assumption is that …

This paper introduces a new lifelong learning solution where a single model is trained for a
sequence of tasks. The main challenge that vision systems face in this context is …

Spectral clustering (SC) has become one of the most widely-adopted clustering algorithms,
and been successfully applied into various applications. We in this work explore the problem …

Fake news has now grown into a big problem for societies and also a major challenge for
people fighting disinformation. This phenomenon plagues democratic elections, reputations …