Convolutional Sparse Coding is improved by heterogeneous uncertainty modeling

Abstract

Aleatoric uncertainty characterizes the variability of features found in natural images, and echoes the epistemic uncertainty ubiquitously found in computer vision models. We explore this ‘‘uncertainty in, uncertainty out’’ relationship by generating convolutional sparse coding dictionaries with parametric epistemic uncertainty. This improves sparseness, resilience and reconstruction of natural images by providing the model a way to explicitly represent the aleatoric uncertainty of its input. We demonstrate how hierarchical processing can make use of this scheme by training a deep convolutional neural network to classify a sparse-coded CIFAR10 dataset, showing that encoding uncertainty in a sparse code is as efficient as using conventional images, with additional beneficial computational properties. Overall, this work empirically demonstrates the advantage of partitioning epistemic uncertainty in sparse coding algorithms.

Type
Publication
ICLR 2023 SNN Workshop
Hugo Ladret
Hugo Ladret
Phd candidate in Computational Neuroscience

During my PhD, I am focusing on the role of precision in natural and artificial neural networks.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.