  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.
