# Chaotic CNN for Limited Data Image Classification

arXiv: https://arxiv.org/abs/2604.14645 · HTML: https://mlsanity.com/paper/2604.14645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 16 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Few-shot Adaptive Faster R-CNN](https://mlsanity.com/paper/1903.09372/) (2019)
- [Towards Dropout Training for Convolutional Neural Networks](https://mlsanity.com/paper/1512.00242/) (2015)
- [ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification](https://mlsanity.com/paper/1910.02423/) (2019)
- [Random Heterogeneous Neurochaos Learning Architecture for Data Classification](https://mlsanity.com/paper/2410.23351/) (2024)
- [Self-Training the Neurochaos Learning Algorithm](https://mlsanity.com/paper/2601.01146/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) often exhibit poor generalisation in limited training data scenarios due to overfitting and insufficient feature diversity. In this work, a simple and effective chaos-based feature transformation is proposed to enhance CNN performance without increasing model complexity. The method applies nonlinear transformations using logistic, skew tent, and sine maps to normalised feature vectors before the classification layer, thereby reshaping the feature space and improving class separability. The approach is evaluated on greyscale datasets (MNIST and Fashion-MNIST) and an RGB dataset (CIFAR-10) using CNN architectures of varying depth under limited data conditions. The results show consistent improvement over the standalone (SA) CNN across all datasets. Notably, a maximum performance gain of 5.43% is achieved on MNIST using the skew tent map with a 3-layer CNN at 40 samples per class. A higher gain of 9.11% is observed on Fashion-MNIST using the sine map with a 3-layer CNN at 50 samples per class. Additionally, a strong gain of 7.47% is obtained on CIFAR-10 using the skew tent map at 200 samples per class. The consistent improvements across different chaotic maps indicate that the performance gain is driven by the shared nonlinear and dynamical properties of chaotic systems. The proposed method is computationally efficient, requires no additional trainable parameters, and can be easily integrated into existing CNN architectures, making it a practical solution for data-scarce image classification tasks.