3 citations · 8 across the 5 of their papers we have counts for
6 papers
Deep Neural Models for color discrimination and color constancy
Alban Flachot, Arash Akbarinia, Heiko H. Schütt +3
Color constancy is our ability to perceive constant colors across varying illuminations. Here, we trained deep neural networks to be color constant and evaluated their performance…
The Utility of Decorrelating Colour Spaces in Vector Quantised Variational Autoencoders
Arash Akbarinia, Raquel Gil-Rodríguez, Alban Flachot +1
Vector quantised variational autoencoders (VQ-VAE) are characterised by three main components: 1) encoding visual data, 2) assigning different vectors in the so-called embeddin…
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar
Arash Akbarinia, Karl R. Gegenfurtner
Machine learning is advancing towards a data-science approach, implying a necessity to a line of investigation to divulge the knowledge learnt by deep neuronal networks. Limiting t…
Manifestation of Image Contrast in Deep Networks
Arash Akbarinia, Karl R. Gegenfurtner
Contrast is subject to dramatic changes across the visual field, depending on the source of light and scene configurations. Hence, the human visual system has evolved to be more se…
How is Contrast Encoded in Deep Neural Networks?
Arash Akbarinia, Karl R. Gegenfurtner
Contrast is a crucial factor in visual information processing. It is desired for a visual system - irrespective of being biological or artificial - to "perceive" the world robustly…
Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons
Arash Akbarinia, C. Alejandro Parraga
Although it seems counter-intuitive, categorical colours do not exist as external physical entities but are very much the product of our brains. Our cortical machinery segments the…