Eye Computer Vision for AI
June 7, 2021
from UTSA, the University of Central Florida (UCF), the Air Force
Research Laboratory (AFRL) and SRI International have developed a new
method that improves how artificial intelligence learns to see.
Led by Sumit Jha, professor in the Department of Computer Science at
UTSA, the team has changed the conventional approach employed in
explaining machine learning decisions that relies on a single injection
of noise into the input layer of a neural network.
The team shows that adding noise—also known as pixilation—along multiple
layers of a network provides a more robust representation of an image
that’s recognized by the AI and creates more robust explanations for AI
decisions. This work aids in the development of what’s been called
“explainable AI” which seeks to enable high-assurance applications of AI
such as medical imaging and autonomous driving.
“This is a big opportunity for UTSA to be part of the global
conversation on how a machine sees.”
“It’s about injecting noise into every layer,” Jha said. “The network is
now forced to learn a more robust representation of the input in all of
its internal layers. If every layer experiences more perturbations in
every training, then the image representation will be more robust and
you won’t see the AI fail just because you change a few pixels of the
Computer vision—the ability to recognize images—has many business
applications. Computer vision can better identify areas of concern in
the livers and brains of cancer patients. This type of machine learning
can also be employed in many other industries. Manufacturers can use it
to detect defection rates, drones can use it to help detect pipeline
leaks, and agriculturists have begun using it to spot early signs of
crop disease to improve their yields.
Through deep learning, a computer is trained to perform behaviors, such
as recognizing speech, identifying images or making predictions. Instead
of organizing data to run through set equations, deep learning works
within basic parameters about a data set and trains the computer to
learn on its own by recognizing patterns using many layers of
The team’s work, led by Jha, is a major advancement to previous work
he’s conducted in this field. In a 2019 paper presented at the AI Safety
workshop co-located with that year’s International Joint Conference on
Artificial Intelligence (IJCAI), Jha, his students and colleagues from
the Oak Ridge National Laboratory demonstrated how poor conditions in
nature can lead to dangerous neural network performance. A computer
vision system was asked to recognize a minivan on a road, and did so
correctly. His team then added a small amount of fog and posed the same
query again to the network: the AI identified the minivan as a fountain.
As a result, their paper was a best paper candidate.
In most models that rely on neural ordinary differential equations (ODEs),
a machine is trained with one input through one network, and then
spreads through the hidden layers to create one response in the output
layer. This team of UTSA, UCF, AFRL and SRI researchers use a more
dynamic approach known as a stochastic differential equations (SDEs).
Exploiting the connection between dynamical systems and show that neural
SDEs lead to less noisy, visually sharper, and quantitatively robust
attributions than those computed using neural ODEs.
SDE approach learns not just from one image but from a set of nearby
images due to the injection of the noise in multiple layers of the
neural network. As more noise is injected, the machine will learn
evolving approaches and find better ways to make explanations or
attributions simply because the model created at the onset is based on
evolving characteristics and/or the conditions of the image. It’s an
improvement on several other attribution approaches including saliency
maps and integrated gradients.
Jha’s new research is
described in the paper “On Smoother Attributions using Neural Stochastic
Differential Equations.” Fellow contributors to this novel approach
include UCF’s Richard Ewetz, AFRL’s Alvaro Velazquez and SRI’s Susmit
Jha. The lab is funded by the Defense Advanced Research Projects Agency,
the Office of Naval Research and the National Science Foundation. Their
research will be presented at the IJCAI 2021, a conference with about a
14% acceptance rate for submissions. Past presenters at this highly
selective conference have included Facebook and Google.
“I am delighted to share the fantastic news that our paper on
explainable AI has just been accepted at IJCAI,” Jha added. “This is a
big opportunity for UTSA to be part of the global conversation on how a