In this paper we show how Group Equivariant Convolutional Neural Networks use
subsampling to learn to break equivariance to their symmetries. We focus on the 2D roto …

Planktonic organisms play a pivotal role within aquatic ecosystems, serving as the
foundation of the aquatic food chain while also playing a critical role in climate regulation …

Rapid assessment and enhanced knowledge of plankton communities and their structures
in the productive upper water column is of crucial importance if we are to understand the …

Deep convolutional neural networks have proven effective in computer vision, especially in
the task of image classification Nevertheless, the success is limited to supervised learning …

Plankton species are of vital importance to the marine food chain. They are susceptible to
minor changes in their environment, which can lead to rapid and devastating changes in the …

Omnidirectional images and spherical representations of $3 D $ shapes cannot be
processed with conventional 2D convolutional neural networks (CNNs) as the unwrapping …

Recent works show that group equivariance as an inductive bias improves neural network
performance for both classification and generation. However, designing group-equivariant …

Zooplankton are key components of the aquatic food web and present a lot of taxonomic
diversity. Over the years, various Machine Learning techniques have been employed for the …

Zooplankton identification has been the subject of many studies. They are mainly based on
the analysis of photographs (computer vision). However, spectroscopic techniques can be a …

Extracting informative features from images has been of capital importance in computer
vision. In this paper, we propose a way to extract such features from images by a method …