A desirable data selection algorithm can efficiently choose the most informative samples to
maximize the utility of limited annotation budgets. However, current approaches …

Recently, the enactment of privacy regulations has promoted the rise of the machine
unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise …

The revolutionary capabilities of large language models (LLMs) have paved the way for
multimodal large language models (MLLMs) and fostered diverse applications across …

With the rapid development of deep learning, training Big Models (BMs) for multiple
downstream tasks becomes a popular paradigm. Researchers have achieved various …

Active learning selects the most informative samples to exploit limited annotation budgets.
Existing work follows a cumbersome pipeline that repeats the time-consuming model …

Convolutional neural networks (CNNs) are used comprehensively in the field of the Internet
of Things (IoTs), such as mobile phones, surveillance, and satellite. However, the …

The emergence of large-scale pre-trained models has heightened their application in
various downstream tasks, yet deployment is a challenge in environments with limited …

In this paper, we present an analytical framework and a novel metric to shed light on the
interpretation of the multimodal vision community. Our approach involves measuring the …

Recently, multi-agent systems that facilitate knowledge sharing among Deep Neural
Network (DNN) agents, have gained increasing attention. This paper explores the dynamics …

Pruning techniques are used comprehensively to compress convolutional neural networks
(CNNs) on image classification. However, the majority of pruning methods require a well pre …