to the human level remains an open challenge for the community.'
author:
- |
    Sanjana Srivastava$^{1, 3}$, Guy Ben-Yosef$^{1, 2}$[^1], Xavier Boix$^{1, 3, 4}$\
    $^1$Center for Brains, Minds, and Machines (CBMM)\
    $^2$Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA\
    $^3$Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA\
    $^4$Children’s Hospital, Harvard Medical School, USA\
    `{sanjanas, gby, xboix}@mit.edu`
bibliography:
- 'iclr2019\_conference.bib'
title: |
    Minimal Images in Deep Neural Networks:\
    Fragile Object Recognition in Natural Images
---

Introduction {#sec:introduction}
============

Deep neural networks (DNNs) have reached tremendous success in recognizing and localizing objects in images. The fundamental approach that led to DNNs consists of building artificial systems based on the brain and human vision. Yet in many important aspects, the capabilities of DNNs are inferior to those of human vision. A promising strand of research is to investigate the similarities and differences, and by bridging the gaps, further improve DNNs [@hassabis2017neuroscience].

Studying cognitive biases and optical illusions is particularly revealing of the function of a visual system, whether natural or artificial. [@Ullman_etal_2016_PNAS] present such a striking phenomenon of human vision, called “minimal images” [@Ullman_etal_2016_PNAS; @Ben-Yosef_etal_2018_Cognition; @Ben-Yosef_Ullman_2018_RSIF]. Minimal images are small regions of an image (*e.g.* 10x10 pixels) 