# Measures of Complexity for Large Scale Image Datasets

arXiv: https://arxiv.org/abs/2008.04431 · HTML: https://mlsanity.com/paper/2008.04431/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 14 of 14)

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- [DiffGAN: A Test Generation Approach for Differential Testing of Deep Neural Networks for Image Analysis](https://mlsanity.com/paper/2410.19794/) (2024)
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## Abstract (arXiv)

Large scale image datasets are a growing trend in the field of machine learning. However, it is hard to quantitatively understand or specify how various datasets compare to each other - i.e., if one dataset is more complex or harder to ``learn'' with respect to a deep-learning based network. In this work, we build a series of relatively computationally simple methods to measure the complexity of a dataset. Furthermore, we present an approach to demonstrate visualizations of high dimensional data, in order to assist with visual comparison of datasets. We present our analysis using four datasets from the autonomous driving research community - Cityscapes, IDD, BDD and Vistas. Using entropy based metrics, we present a rank-order complexity of these datasets, which we compare with an established rank-order with respect to deep learning.