Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-
tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers …

In this paper, we explore a novel knowledge-transfer task, termed as Deep Model
Reassembly (DeRy), for general-purpose model reuse. Given a collection of heterogeneous …

In this paper, we study dataset distillation (DD), from a novel perspective and introduce
a\emph {dataset factorization} approach, termed\emph {HaBa}, which is a plug-and-play …

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 …

Learning to predict agent motions with relationship reasoning is important for many
applications. In motion prediction tasks, maintaining motion equivariance under Euclidean …

Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

Abstract Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by
learning the correlation between the input graphs and labels. However, by presenting a …

Video self-supervised learning is a challenging task, which requires significant expressive
power from the model to leverage rich spatial-temporal knowledge and generate effective …

Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …

In this paper, we explore a novel model reusing task tailored for graph neural networks
(GNNs), termed as" deep graph reprogramming". We strive to reprogram a pre-trained GNN …