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Neural Lineage

Runpeng Yu · Xinchao Wang

Arch 4A-E Poster #82
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT
Oral presentation: Orals 2B Deep learning architectures and techniques
Wed 19 Jun 1 p.m. PDT — 2:30 p.m. PDT


Given a well-behaved neural network, is possibleto 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, effectively identifying not only parent models but also their ancestors.

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