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 …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Transfer learning is a pervasive technology in computer vision and natural language
processing fields, yielding exponential performance improvements by leveraging prior …

Domain adaptive semantic segmentation attempts to make satisfactory dense predictions on
an unlabeled target domain by utilizing the supervised model trained on a labeled source …

Deep neural networks (DNNs) achieve state-of-the-art performance in many areas, including
computer vision, system configuration, and question-answering. However, DNNs are …

Transfer learning (TL) utilizes data or knowledge from one or more source domains to
facilitate learning in a target domain. It is particularly useful when the target domain has very …

The success of deep learning algorithms generally depends on large-scale data, while
humans appear to have inherent ability of knowledge transfer, by recognizing and applying …

This paper focuses on model transferability estimation, ie, assessing the performance of pre-
trained models on a downstream task without performing fine-tuning. Motivated by the …

Deep models must learn robust and transferable representations in order to perform well on
new domains. While domain transfer methods (eg, domain adaptation, domain …

Transfer learning has become a popular method for leveraging pre-trained models in
computer vision. However, without performing computationally expensive fine-tuning, it is …