# Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification

arXiv: https://arxiv.org/abs/2209.13233 · HTML: https://mlsanity.com/paper/2209.13233/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 87 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location](https://mlsanity.com/paper/2003.07064/) (2020)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks](https://mlsanity.com/paper/1910.01663/) (2019)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [A Close Look at Deep Learning with Small Data](https://mlsanity.com/paper/2003.12843/) (2020)
- [Deep Forest](https://mlsanity.com/paper/1702.08835/) (2017)
- [Deep Learning on Small Datasets without Pre-Training using Cosine Loss](https://mlsanity.com/paper/1901.09054/) (2019)
- [OL'E: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning](https://mlsanity.com/paper/1712.01727/) (2017)
- [Scattering Networks for Hybrid Representation Learning](https://mlsanity.com/paper/1809.06367/) (2018)
- [Real-time Federated Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2003.02793/) (2020)
- [Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes](https://mlsanity.com/paper/1405.1207/) (2014)
- [Distilling Visual Priors from Self-Supervised Learning](https://mlsanity.com/paper/2008.00261/) (2020)
- [Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification](https://mlsanity.com/paper/2108.13122/) (2021)
- [VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges](https://mlsanity.com/paper/2103.03768/) (2021)
- [Lights and Shadows in Evolutionary Deep Learning: Taxonomy, Critical Methodological Analysis, Cases of Study, Learned Lessons, Recommendations and Challenges](https://mlsanity.com/paper/2008.03620/) (2020)
- [A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends](https://mlsanity.com/paper/2209.06399/) (2022)
- [VIPriors 2: Visual Inductive Priors for Data-Efficient Deep Learning Challenges](https://mlsanity.com/paper/2201.08625/) (2022)
- [Fast Low-rank Shared Dictionary Learning for Image Classification](https://mlsanity.com/paper/1610.08606/) (2016)
- [Harmonic Networks with Limited Training Samples](https://mlsanity.com/paper/1905.00135/) (2019)
- [Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning](https://mlsanity.com/paper/2012.09444/) (2020)

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

- [Sharpness-Aware Minimization for Evolutionary Feature Construction in Regression](https://mlsanity.com/paper/2405.06869/) (2024)
- [Coevolution of Camouflage](https://mlsanity.com/paper/2304.11793/) (2023)
- [Evolutionary Causal Discovery with Relative Impact Stratification for Interpretable Data Analysis](https://mlsanity.com/paper/2404.16361/) (2024)
- [Multi-Representation Genetic Programming: A Case Study on Tree-based and Linear Representations](https://mlsanity.com/paper/2405.14268/) (2024)

## Abstract (arXiv)

Data-efficient image classification is a challenging task that aims to solve image classification using small training data. Neural network-based deep learning methods are effective for image classification, but they typically require large-scale training data and have major limitations such as requiring expertise to design network architectures and having poor interpretability. Evolutionary deep learning is a recent hot topic that combines evolutionary computation with deep learning. However, most evolutionary deep learning methods focus on evolving architectures of neural networks, which still suffer from limitations such as poor interpretability. To address this, this paper proposes a new genetic programming-based evolutionary deep learning approach to data-efficient image classification. The new approach can automatically evolve variable-length models using many important operators from both image and classification domains. It can learn different types of image features from colour or gray-scale images, and construct effective and diverse ensembles for image classification. A flexible multi-layer representation enables the new approach to automatically construct shallow or deep models/trees for different tasks and perform effective transformations on the input data via multiple internal nodes. The new approach is applied to solve five image classification tasks with different training set sizes. The results show that it achieves better performance in most cases than deep learning methods for data-efficient image classification. A deep analysis shows that the new approach has good convergence and evolves models with high interpretability, different lengths/sizes/shapes, and good transferability.