Neural Lineage

摘要

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.