# Optimizing Data Curation through Spectral Analysis and Joint Batch Selection (SALN)

arXiv: https://arxiv.org/abs/2412.17069 · HTML: https://mlsanity.com/paper/2412.17069/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Selection via Proxy: Efficient Data Selection for Deep Learning](https://mlsanity.com/paper/1906.11829/) (2019)
- [Batch Active Learning at Scale](https://mlsanity.com/paper/2107.14263/) (2021)
- [Some Improvements on Deep Convolutional Neural Network Based Image Classification](https://mlsanity.com/paper/1312.5402/) (2013)
- [Data curation via joint example selection further accelerates multimodal learning](https://mlsanity.com/paper/2406.17711/) (2024)
- [The Effects of Data Quality on Machine Learning Performance on Tabular Data](https://mlsanity.com/paper/2207.14529/) (2022)

## Abstract (arXiv)

In modern deep learning models, long training times and large datasets present significant challenges to both efficiency and scalability. Effective data curation and sample selection are crucial for optimizing the training process of deep neural networks. This paper introduces SALN, a method designed to prioritize and select samples within each batch rather than from the entire dataset. By utilizing jointly selected batches, SALN enhances training efficiency compared to independent batch selection. The proposed method applies a spectral analysis-based heuristic to identify the most informative data points within each batch, improving both training speed and accuracy. The SALN algorithm significantly reduces training time and enhances accuracy when compared to traditional batch prioritization or standard training procedures. It demonstrates up to an 8x reduction in training time and up to a 5% increase in accuracy over standard training methods. Moreover, SALN achieves better performance and shorter training times compared to Google's JEST method developed by DeepMind.