# Gramian Angular Fields for leveraging pretrained computer vision models with anomalous diffusion trajectories

arXiv: https://arxiv.org/abs/2310.01416 · HTML: https://mlsanity.com/paper/2310.01416/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 91 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Imaging Time-Series to Improve Classification and Imputation](https://mlsanity.com/paper/1506.00327/) (2015)
- [Machine learning method for single trajectory characterization](https://mlsanity.com/paper/1903.02850/) (2019)
- [Implementation of Deep Neural Networks to Classify EEG Signals using Gramian Angular Summation Field for Epilepsy Diagnosis](https://mlsanity.com/paper/2003.04534/) (2020)
- [Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories](https://mlsanity.com/paper/2108.02834/) (2021)
- [Characterization of anomalous diffusion through convolutional transformers](https://mlsanity.com/paper/2210.04959/) (2022)
- [A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data](https://mlsanity.com/paper/2101.05891/) (2021)

## Built on by (most cited first, 3 of 3)

- [NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imaging](https://mlsanity.com/paper/2310.04429/) (2023)
- [Exploring Driving Behavior for Autonomous Vehicles Based on Gramian Angular Field Vision Transformer](https://mlsanity.com/paper/2310.13906/) (2023)
- [AnomalousNet: A Hybrid Approach with Attention U-Nets and Change Point Detection for Accurate Characterization of Anomalous Diffusion in Video Data](https://mlsanity.com/paper/2504.05271/) (2025)

## Abstract (arXiv)

Anomalous diffusion is present at all scales, from atomic to large scales. Some exemplary systems are; ultra-cold atoms, telomeres in the nucleus of cells, moisture transport in cement-based materials, the free movement of arthropods, and the migration patterns of birds. The characterization of the diffusion gives critical information about the dynamics of these systems and provides an interdisciplinary framework with which to study diffusive transport. Thus, the problem of identifying underlying diffusive regimes and inferring the anomalous diffusion exponent α with high confidence is critical to physics, chemistry, biology, and ecology. Classification and analysis of raw trajectories combining machine learning techniques with statistics extracted from them have widely been studied in the Anomalous Diffusion Challenge ge (Munoz-Gil et al., 2021). Here we present a new data-driven method for working with diffusive trajectories. This method utilizes Gramian Angular Fields (GAF) to encode one-dimensional trajectories as images (Gramian Matrices), while preserving their spatiotemporal structure for input to computer-vision models. This allows us to leverage two well-established pre-trained computer-vision models, ResNet and MobileNet, to characterize the underlying diffusive regime, and infer the anomalous diffusion exponent α. Short raw trajectories, of lengths between 10 and 50, are commonly encountered in single-particle tracking experiments and are the most difficult to characterize. We show that by using GAF images, we can outperform the current state-of-the-art while increasing accessibility to machine learning methods in an applied setting.