# How much data do you need? Part 2: Predicting DL class specific training dataset sizes

arXiv: https://arxiv.org/abs/2403.06311 · HTML: https://mlsanity.com/paper/2403.06311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 10 Mar 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)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Deep Learning Scaling is Predictable, Empirically](https://mlsanity.com/paper/1712.00409/) (2017)
- [Beyond neural scaling laws: beating power law scaling via data pruning](https://mlsanity.com/paper/2206.14486/) (2022)
- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)
- [Revisiting Neural Scaling Laws in Language and Vision](https://mlsanity.com/paper/2209.06640/) (2022)
- [Learning Curve Theory](https://mlsanity.com/paper/2102.04074/) (2021)
- [How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?](https://mlsanity.com/paper/1511.06348/) (2015)
- [How Much More Data Do I Need? Estimating Requirements for Downstream Tasks](https://mlsanity.com/paper/2207.01725/) (2022)

## Built on by (most cited first, 1 of 1)

- [Neural Scaling Laws for Deep Regression](https://mlsanity.com/paper/2509.10000/) (2025)

## Abstract (arXiv)

This paper targets the question of predicting machine learning classification model performance, when taking into account the number of training examples per class and not just the overall number of training examples. This leads to the a combinatorial question, which combinations of number of training examples per class should be considered, given a fixed overall training dataset size. In order to solve this question, an algorithm is suggested which is motivated from special cases of space filling design of experiments. The resulting data are modeled using models like powerlaw curves and similar models, extended like generalized linear models i.e. by replacing the overall training dataset size by a parametrized linear combination of the number of training examples per label class. The proposed algorithm has been applied on the CIFAR10 and the EMNIST datasets.