Slide 21
Slide 21 text
Human-like, or deeply weird?3
bstract
s (DNNs) have recently been
performance on a variety of
most notably visual classification
Ns are now able to classify objects
an-level performance, questions
differences remain between com-
A recent study [30] revealed that
a lion) in a way imperceptible to
to label the image as something
eling a lion a library). Here we
easy to produce images that are
e to humans, but that state-of-the-
ecognizable objects with 99.99%
with certainty that white noise
ally, we take convolutional neu-
form well on either the ImageNet
en find images with evolutionary
cent that DNNs label with high
o each dataset class. It is possi-
lly unrecognizable to human eyes
ar certainty are familiar objects,
ages” (more generally, fooling ex- Figure 1. Evolved images that are unrecognizable to humans,
3 Nguyen 2015. Deep Neural Networks are Easily Fooled: High Confidence Predictions for
Unrecognizable Images. Computer Vision and Pattern Recognition, IEEE, 2015.