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.

Related papers

• Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond [61.18736646013446]
  In pursuit of a deeper understanding of its surprising behaviors, we investigate the utility of a simple yet accurate model of a trained neural network. Across three case studies, we illustrate how it can be applied to derive new empirical insights on a diverse range of prominent phenomena.
  arXiv  Detail & Related papers  (2024-10-31T22:54:34Z)
• Learning to Learn with Generative Models of Neural Network Checkpoints [71.06722933442956]
  We construct a dataset of neural network checkpoints and train a generative model on the parameters. We find that our approach successfully generates parameters for a wide range of loss prompts. We apply our method to different neural network architectures and tasks in supervised and reinforcement learning.
  arXiv  Detail & Related papers  (2022-09-26T17:59:58Z)
• Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling [20.296566563098057]
  Sparse modeling for signal processing and machine learning has been at the focus of scientific research for over two decades. This article reviews some recent advances in incorporating sparsity-promoting priors into three popular data modeling tools.
  arXiv  Detail & Related papers  (2022-05-28T00:43:52Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• A Distillation Learning Model of Adaptive Structural Deep Belief Network for AffectNet: Facial Expression Image Database [0.0]
  We have developed the adaptive structure learning method of Deep Belief Network (DBN) In this paper, our model is applied to a facial expression image data set, AffectNet. The classification accuracy was improved from 78.4% to 91.3% by the proposed method.
  arXiv  Detail & Related papers  (2021-10-25T08:01:36Z)
• Firearm Detection via Convolutional Neural Networks: Comparing a Semantic Segmentation Model Against End-to-End Solutions [68.8204255655161]
  Threat detection of weapons and aggressive behavior from live video can be used for rapid detection and prevention of potentially deadly incidents. One way for achieving this is through the use of artificial intelligence and, in particular, machine learning for image analysis. We compare a traditional monolithic end-to-end deep learning model and a previously proposed model based on an ensemble of simpler neural networks detecting fire-weapons via semantic segmentation.
  arXiv  Detail & Related papers  (2020-12-17T15:19:29Z)
• A Bayesian Perspective on Training Speed and Model Selection [51.15664724311443]
  We show that a measure of a model's training speed can be used to estimate its marginal likelihood. We verify our results in model selection tasks for linear models and for the infinite-width limit of deep neural networks. Our results suggest a promising new direction towards explaining why neural networks trained with gradient descent are biased towards functions that generalize well.
  arXiv  Detail & Related papers  (2020-10-27T17:56:14Z)
• Similarity of Neural Networks with Gradients [8.804507286438781]
  We propose to leverage both feature vectors and gradient ones into designing the representation of a neural network. We show that the proposed approach provides a state-of-the-art method for computing similarity of neural networks.
  arXiv  Detail & Related papers  (2020-03-25T17:04:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.