Neural Lineage

• URL: http://arxiv.org/abs/2406.11129v1
• Date: Mon, 17 Jun 2024 01:11:53 GMT
• Title: Neural Lineage
• Authors: Runpeng Yu, Xinchao Wang,
• Abstract summary: We introduce a novel task known as neural lineage detection, aiming at discovering lineage relationships between parent and child models. For practical convenience, we introduce a learning-free approach, which integrates an approximation of the finetuning process into the neural network representation similarity metrics. For the pursuit of accuracy, we introduce a learning-based lineage detector comprising encoders and a transformer detector.
• Score: 56.34149480207817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a well-behaved neural network, is possible to identify its parent, based on which it was tuned? In this paper, we introduce a novel task known as neural lineage detection, aiming at discovering lineage relationships between parent and child models. Specifically, from a set of parent models, neural lineage detection predicts which parent model a child model has been fine-tuned from. We propose two approaches to address this task. (1) For practical convenience, we introduce a learning-free approach, which integrates an approximation of the finetuning process into the neural network representation similarity metrics, leading to a similarity-based lineage detection scheme. (2) For the pursuit of accuracy, we introduce a learning-based lineage detector comprising encoders and a transformer detector. Through experimentation, we have validated that our proposed learning-free and learning-based methods outperform the baseline in various learning settings and are adaptable to a variety of visual models. Moreover, they also exhibit the ability to trace cross-generational lineage, identifying not only parent models but also their ancestors.

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