The development of information gathering and extraction technology has led to the
popularity of multi-view data, which enables samples to be seen from numerous …

The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

In this paper, we explore a novel and ambitious knowledge-transfer task, termed Knowledge
Factorization (KF). The core idea of KF lies in the modularization and assemblability of …

For future learning systems, incremental learning is desirable because it allows for: efficient
resource usage by eliminating the need to retrain from scratch at the arrival of new data; …

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 …

Transferring knowledge from a teacher neural network pretrained on the same or a similar
task to a student neural network can significantly improve the performance of the student …

Abstract Machine Learning (ML) models are increasingly deployed in the wild to perform a
wide range of tasks. In this work, we ask to what extent can an adversary steal functionality …

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 …

Deep neural networks with millions of parameters may suffer from poor generalization due to
overfitting. To mitigate the issue, we propose a new regularization method that penalizes the …

We propose an efficient and unified framework, namely ThiNet, to simultaneously accelerate
and compress CNN models in both training and inference stages. We focus on the filter level …