Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks
Jiacong Hu, Jing Gao, Jingwen Ye, Yang Gao, Xingen Wang, Zunlei Feng,, Mingli Song

TL;DR
This paper introduces Model Disassembling and Assembling (MDA), a novel method inspired by biological visual systems that enables creating new models by reusing task-aware components from trained CNNs without additional training.
Contribution
It proposes a new paradigm for model creation and reuse by disassembling CNNs into components and assembling them into tailored models, inspired by biological visual pathways.
Findings
Disassembled components match or outperform baseline models.
Assembled models demonstrate effective task adaptation.
MDA enables diverse applications like model compression and knowledge distillation.
Abstract
With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the…
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Taxonomy
TopicsVisual Attention and Saliency Detection · Advanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques
