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researcher

J. Dawson

33 papers hereh-index 221.5k citations116 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author28
  • last author5

Across the 33 of 33 papers where every author was matched, so the position is known.

fields
  • cs.CV27
  • cs.LG5
  • eess.AS1
same name
  • J. Dawson — 49 papers, h 17
  • J. Dawson — 49 papers, h 29
  • J. Dawson — 40 papers, h 16
  • J. Dawson — 17 papers, h 4
  • J. Dawson — 13 papers, h 7
  • J. Dawson — 12 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most cited3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition

119 citations · 134 across the 20 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.LG2019

SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations

Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani +2

Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of ℓp​-bounded and $\…

cs.CV2019

Deep Sparse Band Selection for Hyperspectral Face Recognition

Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi

Hyperspectral imaging systems collect and process information from specific wavelengths across the electromagnetic spectrum. The fusion of multi-spectral bands in the visible spect…

cs.CV2019

Defending Against Adversarial Iris Examples Using Wavelet Decomposition

Sobhan Soleymani, Ali Dabouei, Jeremy Dawson +1

Deep neural networks have presented impressive performance in biometric applications. However, their performance is highly at risk when facing carefully crafted input samples known…

cs.LG2019

Adversarial Examples to Fool Iris Recognition Systems

Sobhan Soleymani, Ali Dabouei, Jeremy Dawson +1

Adversarial examples have recently proven to be able to fool deep learning methods by adding carefully crafted small perturbation to the input space image. In this paper, we study…

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