# Efficient Unsupervised Learning for Plankton Images

arXiv: https://arxiv.org/abs/2209.06726 · HTML: https://mlsanity.com/paper/2209.06726/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 43 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Exploiting Cyclic Symmetry in Convolutional Neural Networks](https://mlsanity.com/paper/1602.02660/) (2016)
- [MorphoCluster: Efficient Annotation of Plankton images by Clustering](https://mlsanity.com/paper/2005.01595/) (2020)
- [Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning](https://mlsanity.com/paper/1805.10106/) (2018)

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

- [Survey of Automatic Plankton Image Recognition: Challenges, Existing Solutions and Future Perspectives](https://mlsanity.com/paper/2305.11739/) (2023)
- [Diffusion Representations for Fine-Grained Image Classification: A Marine Plankton Case Study](https://mlsanity.com/paper/2601.13416/) (2026)
- [Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design](https://mlsanity.com/paper/2409.02648/) (2024)

## Abstract (arXiv)

Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that can reflect into consequent morphological and dynamical modifications. Nowadays, the availability of advanced automatic or semi-automatic acquisition systems has been allowing the production of an increasingly large amount of plankton image data. The adoption of machine learning algorithms to classify such data may be affected by the significant cost of manual annotation, due to both the huge quantity of acquired data and the numerosity of plankton species. To address these challenges, we propose an efficient unsupervised learning pipeline to provide accurate classification of plankton microorganisms. We build a set of image descriptors exploiting a two-step procedure. First, a Variational Autoencoder (VAE) is trained on features extracted by a pre-trained neural network. We then use the learnt latent space as image descriptor for clustering. We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images. The proposed pipeline outperforms the benchmark algorithms for all the plankton datasets included in our analysis, providing better image embedding properties.