activity
20152017
most citedNO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles

209 citations · 353 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV201750 cited

Standard detectors aren't (currently) fooled by physical adversarial stop signs

Jiajun Lu, Hussein Sibai, Evan Fabry +1

An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time. If adversarial examples existed that could fool a de…

cs.HC201771 cited

Rotation Blurring: Use of Artificial Blurring to Reduce Cybersickness in Virtual Reality First Person Shooters

Pulkit Budhiraja, Mark Roman Miller, Abhishek K Modi +1

Users of Virtual Reality (VR) systems often experience vection, the perception of self-motion in the absence of any physical movement. While vection helps to improve presence in VR…

cs.CV2017209 cited

NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles

Jiajun Lu, Hussein Sibai, Evan Fabry +1

It has been shown that most machine learning algorithms are susceptible to adversarial perturbations. Slightly perturbing an image in a carefully chosen direction in the image spac…

cs.CV2016

Swapout: Learning an ensemble of deep architectures

Saurabh Singh, Derek Hoiem, David Forsyth

We describe Swapout, a new stochastic training method, that outperforms ResNets of identical network structure yielding impressive results on CIFAR-10 and CIFAR-100. Swapout sample…

cs.HC201523 cited

Where's My Drink? Enabling Peripheral Real World Interactions While Using HMDs

Pulkit Budhiraja, Rajinder Sodhi, Brett Jones +3

Head Mounted Displays (HMDs) allow users to experience virtual reality with a great level of immersion. However, even simple physical tasks like drinking a beverage can be difficul…