# IT-OSE: Exploring Optimal Sample Size for Industrial Data Augmentation

arXiv: https://arxiv.org/abs/2602.15878 · HTML: https://mlsanity.com/paper/2602.15878/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 3 Feb 2026.

## Builds on (most cited first, 8 of 8)

- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [Diffusion-TS: Interpretable Diffusion for General Time Series Generation](https://mlsanity.com/paper/2403.01742/) (2024)
- [Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models](https://mlsanity.com/paper/2506.09042/) (2025)
- [On the Constrained Time-Series Generation Problem](https://mlsanity.com/paper/2307.01717/) (2023)
- [Utilizing Image Transforms and Diffusion Models for Generative Modeling of Short and Long Time Series](https://mlsanity.com/paper/2410.19538/) (2024)
- [SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents](https://mlsanity.com/paper/2509.06283/) (2025)
- [Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher](https://mlsanity.com/paper/2004.08780/) (2020)
- [Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey](https://mlsanity.com/paper/2310.10060/) (2023)

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

- [Feature Recalibration Based Olfactory-Visual Multimodal Model for Enhanced Rice Deterioration Detection](https://mlsanity.com/paper/2602.14408/) (2026)
- [SoDa2: Single-Stage Open-Set Domain Adaptation via Decoupled Alignment for Cross-Scene Hyperspectral Image Classification](https://mlsanity.com/paper/2605.03371/) (2026)
- [R2H-Diff: Guided Spectral Diffusion Model for RGB-to-Hyperspectral Reconstruction](https://mlsanity.com/paper/2605.05688/) (2026)
- [SAGE: Subpopulation-Aware Generative Enhancement for Mitigating Spurious Correlations](https://mlsanity.com/paper/2609.01051/) (2026)

## Abstract (arXiv)

In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. There is no theoretical research or established estimation for the optimal sample size (OSS) in augmentation, nor is there an established metric to evaluate the accuracy of OSS or its deviation from the ground truth. To address these issues, we propose an information-theoretic optimal sample size estimation (IT-OSE) to provide reliable OSS estimation for industrial data augmentation. An interval coverage and deviation (ICD) score is proposed to evaluate the estimated OSS intuitively. The relationship between OSS and dominant factors is theoretically analyzed and formulated, thereby enhancing the interpretability. Experiments show that, compared to empirical estimation, the IT-OSE increases accuracy in classification tasks across baseline models by an average of 4.38%, and reduces MAPE in regression tasks across baseline models by an average of 18.80%. The improvements in downstream model performance are more stable. ICDdev in the ICD score is also reduced by an average of 49.30%. The determinism of OSS is enhanced. Compared to exhaustive search, the IT-OSE achieves the same OSS while reducing computational and data costs by an average of 83.97% and 93.46%. Furthermore, practicality experiments demonstrate that the IT-OSE exhibits generality across representative sensor-based industrial scenarios.