Efficient Unsupervised Learning for Plankton Images. (arXiv:2209.06726v1 [cs.CV])

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 …

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