# Beyond Nearest Neighbor Interpolation in Data Augmentation

arXiv: https://arxiv.org/abs/2504.01527 · HTML: https://mlsanity.com/paper/2504.01527/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Image Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2204.08610/) (2022)
- [A Bayesian Data Augmentation Approach for Learning Deep Models](https://mlsanity.com/paper/1710.10564/) (2017)
- [Semantic Segmentation with Labeling Uncertainty and Class Imbalance](https://mlsanity.com/paper/2102.04566/) (2021)
- [Evaluation of Rounding Functions in Nearest-Neighbor Interpolation](https://mlsanity.com/paper/2003.06885/) (2020)
- [Effects of Image Size on Deep Learning](https://mlsanity.com/paper/2101.11508/) (2021)
- [Normalized Weighting Schemes for Image Interpolation Algorithms](https://mlsanity.com/paper/2011.08559/) (2020)
- [Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning](https://mlsanity.com/paper/2302.11522/) (2023)
- [Convolutional Neural Networks for Automatic Detection of Intact Adenovirus from TEM Imaging with Debris, Broken and Artefacts Particles](https://mlsanity.com/paper/2310.19630/) (2023)
- [Stochastic Rounding for Image Interpolation and Scan Conversion](https://mlsanity.com/paper/2110.12983/) (2021)

## Built on by (most cited first, 2 of 2)

- [Toward Optimal Adenovirus Detection Using YOLO26](https://mlsanity.com/paper/2607.17799/) (2026)
- [Stride Independent Patching for Deep Learning](https://mlsanity.com/paper/2610.12216/) (2026)

## Abstract (arXiv)

Avoiding the risk of undefined categorical labels using nearest neighbor interpolation overlooks the risk of exacerbating pixel level annotation errors in augmented training data. Additionally, the inherent low pass filtering effects of interpolation algorithms exacerbate the risk of degrading high frequency structural details within annotated regions of interest. To avoid these risks, the author modified convolutional neural networks data transformation functions by incorporating a modified geometric transformation function, removing reliance on nearest neighbor interpolation, and integrating a mean-based class filtering mechanism to handle undefined categorical labels with alternative interpolation algorithms. The author also implemented an offline data augmentation pipeline to generate interpolation specific augmented training data, enabling quantitative assessment of interpolation specific low pass filtering effects on augmented training data. Experimental evaluation on three medical image segmentation datasets and the XBAT+ datasets demonstrated performance gains across multiple quantitative metrics.