# Exploring Cross-model Neuronal Correlations in the Context of Predicting Model Performance and Generalizability

arXiv: https://arxiv.org/abs/2408.08448 · HTML: https://mlsanity.com/paper/2408.08448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 42 references that are in the map.
- First posted on arXiv 15 Aug 2024.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Adversarial Examples Are Not Bugs, They Are Features](https://mlsanity.com/paper/1905.02175/) (2019)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Revisiting the Calibration of Modern Neural Networks](https://mlsanity.com/paper/2106.07998/) (2021)
- [Insights on representational similarity in neural networks with canonical correlation](https://mlsanity.com/paper/1806.05759/) (2018)
- [Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers](https://mlsanity.com/paper/1901.03006/) (2019)
- [Feature-Guided Black-Box Safety Testing of Deep Neural Networks](https://mlsanity.com/paper/1710.07859/) (2017)
- [A Survey on AI Assurance](https://mlsanity.com/paper/2111.07505/) (2021)
- [Regulatory Markets for AI Safety](https://mlsanity.com/paper/2001.00078/) (2019)
- [Systematic Literature Review of Validation Methods for AI Systems](https://mlsanity.com/paper/2107.12190/) (2021)
- [Afro-MNIST: Synthetic generation of MNIST-style datasets for low-resource languages](https://mlsanity.com/paper/2009.13509/) (2020)
- [Interpretation of Neural Networks is Susceptible to Universal Adversarial Perturbations](https://mlsanity.com/paper/2212.03095/) (2022)

## Built on by (most cited first, 3 of 3)

- [Architecture-Dependent Causal Transfer of Activation States Across Large Language Models](https://mlsanity.com/paper/2608.16347/) (2026)
- [Transformer-Based Bearing Fault Detection using Temporal Decomposition Attention Mechanism](https://mlsanity.com/paper/2412.11245/) (2024)
- [Exploring the Impact of Dataset Statistical Effect Size on Model Performance and Data Sample Size Sufficiency](https://mlsanity.com/paper/2501.02673/) (2025)

## Abstract (arXiv)

As Artificial Intelligence (AI) models are increasingly integrated into critical systems, the need for a robust framework to establish the trustworthiness of AI is increasingly paramount. While collaborative efforts have established conceptual foundations for such a framework, there remains a significant gap in developing concrete, technically robust methods for assessing AI model quality and performance. This paper introduces a novel approach for assessing a newly trained model's performance based on another known model by calculating correlation between neural networks. The proposed method evaluates correlations by determining if, for each neuron in one network, there exists a neuron in the other network that produces similar output. This approach has implications for memory efficiency, allowing for the use of smaller networks when high correlation exists between networks of different sizes. Experiments on five fully connected networks and a two layer CNN trained on MNIST family datasets show that higher alignment with the CNN tracks stronger performance and smaller degradation under black box transfer based attacks. On ImageNet pretrained ResNets and DenseNets, partial layer comparisons recover intuitive architectural affinities, indicating that the procedure scales with reasonable approximations. These results support representational alignment as a lightweight compatibility check that complements standard accuracy, calibration, and robustness evaluations and enables early external validation of new models. Code is available at https://github.com/aheldis/Cross-model-Correlation.git.