# An empirical study of the relation between network architecture and complexity

arXiv: https://arxiv.org/abs/1911.04120 · HTML: https://mlsanity.com/paper/1911.04120/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Characterizing and Measuring the Similarity of Neural Networks with Persistent Homology](https://mlsanity.com/paper/2101.07752/) (2021)
- [Persistent Homology Captures the Generalization of Neural Networks Without A Validation Set](https://mlsanity.com/paper/2106.00012/) (2021)
- [problexity -- an open-source Python library for binary classification problem complexity assessment](https://mlsanity.com/paper/2207.06709/) (2022)
- [k-NN as a Simple and Effective Estimator of Transferability](https://mlsanity.com/paper/2503.18528/) (2025)

## Abstract (arXiv)

In this preregistration submission, we propose an empirical study of how networks handle changes in complexity of the data. We investigate the effect of network capacity on generalization performance in the face of increasing data complexity. For this, we measure the generalization error for an image classification task where the number of classes steadily increases. We compare a number of modern architectures at different scales in this setting. The methodology, setup, and hypotheses described in this proposal were evaluated by peer review before experiments were conducted.