92 citations · 163 across the 12 of their papers we have counts for
6 papers · 1 filter
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks
Anne Harrington, Arturo Deza
Recent work suggests that representations learned by adversarially robust networks are more human perceptually-aligned than non-robust networks via image manipulations. Despite app…
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation
Binxu Wang, David Mayo, Arturo Deza +2
Self-supervised learning is a powerful way to learn useful representations from natural data. It has also been suggested as one possible means of building visual representation in…
Emergent Properties of Foveated Perceptual Systems
Arturo Deza, Talia Konkle
The goal of this work is to characterize the representational impact that foveation operations have for machine vision systems, inspired by the foveated human visual system, which…
Assessment of Faster R-CNN in Man-Machine collaborative search
Arturo Deza, Amit Surana, Miguel P. Eckstein
With the advent of modern expert systems driven by deep learning that supplement human experts (e.g. radiologists, dermatologists, surveillance scanners), we analyze how and when d…
Towards Metamerism via Foveated Style Transfer
Arturo Deza, Aditya Jonnalagadda, Miguel Eckstein
The problem of is defined as finding a family of perceptually indistinguishable, yet physically different images. In this paper, we propose our NeuroFo…
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Arturo Deza, Miguel P. Eckstein
Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that…