# Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning

arXiv: https://arxiv.org/abs/2407.15724 · HTML: https://mlsanity.com/paper/2407.15724/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis](https://mlsanity.com/paper/2010.14925/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [A Comparative Survey of Deep Active Learning](https://mlsanity.com/paper/2203.13450/) (2022)
- [You Only Condense Once: Two Rules for Pruning Condensed Datasets](https://mlsanity.com/paper/2310.14019/) (2023)
- [Dataset Distillation via Adversarial Prediction Matching](https://mlsanity.com/paper/2312.08912/) (2023)
- [Domain-guided data augmentation for deep learning on medical imaging](https://mlsanity.com/paper/2210.04977/) (2022)
- [Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data](https://mlsanity.com/paper/2101.01441/) (2021)

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

- [X-Factor: Quality Is a Dataset-Intrinsic Property](https://mlsanity.com/paper/2505.22813/) (2025)
- [Generative deep learning for foundational video translation in ultrasound](https://mlsanity.com/paper/2511.03255/) (2025)
- [Which Similarity-Sensitive Entropy (Sentropy)?](https://mlsanity.com/paper/2511.03849/) (2025)

## Abstract (arXiv)

In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practiceunicodex2013maximizing dataset size and class balanceunicodex2013does not guarantee dataset diversity. We hypothesized that, for a given model architecture, model performance can be improved by maximizing diversity more directly. To test this hypothesis, we introduce a comprehensive framework of diversity measures from ecology that generalizes familiar quantities like Shannon entropy by accounting for similarities among images. (Size and class balance emerge as special cases.) Analyzing thousands of subsets from seven medical datasets showed that the best correlates of performance were not size or class balance but Aunicodex2013"big alpha"unicodex2013a set of generalized entropy measures interpreted as the effective number of image-class pairs in the dataset, after accounting for image similarities. One of these, A0, explained 67% of the variance in balanced accuracy, vs. 54% for class balance and just 39% for size. The best pair of measures was size-plus-A1 (79%), which outperformed size-plus-class-balance (74%). Subsets with the largest A0 performed up to 16% better than those with the largest size (median improvement, 8%). We propose maximizing A as a way to improve deep learning performance in medical imaging.