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.

通过引入一种称为神经谱系检测的新任务，本文研究了如何根据经过调优的神经网络来确定其父模型。我们提出了两种方法来解决该任务：一种是基于相似度的谱系检测方案，将微调过程的近似结合到神经网络表示相似性度量中；另一种是基于学习的谱系探测器，由编码器和变换器探测器组成。经过实验证实，我们的学习自由和学习式方法在各种学习场景中均优于基准模型，并且适用于各种视觉模型，可以追踪不仅父模型还包括其祖先的跨代谱系。