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 …

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

This paper studies task adaptive pre-trained model selection, an underexplored problem of
assessing pre-trained models for the target task and select best ones from the model …

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 …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …

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

Abstract Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative
for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only …

Intermediate task fine-tuning has been shown to culminate in large transfer gains across
many NLP tasks. With an abundance of candidate datasets as well as pre-trained language …