IEEE Computational Intelligence Magazine - August 2019 - 4

Publication
Spotlight

Haibo He, Jon Garibaldi, Kay Chen Tan,
Julian Togelius, Yaochu Jin, and
Yew Soon Ong

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

Deep CNN-Based Blind Image Quality
Predictor, by J. Kim, A.-D. Nguyen,
and S. Lee, IEEE Transactions on Neural Networks and Learning Systems, Vol.
30, No. 1, January 2019, pp. 11-24.
Digital Object Identifier: 10.1109/
TNNLS.2018.2829819
"Image recognition based on convolutional neural networks (CNNs) has
recently been shown to deliver the stateof-the-art performance in various areas
of computer vision and image processing. Nevertheless, applying a deep CNN
to no-reference image quality assessment (NR-IQA) remains a challenging
task due to critical obstacles, i.e., the lack
of a training database. In this paper, we
propose a CNN-based NR-IQA framework that can effectively solve this problem. The proposed method-deep image
quality assessor (DIQA)-separates the
training of NR-IQA into two stages: (1)
an objective distortion part and (2) a
human visual system-related part. In the
first stage, the CNN learns to predict
the objective error map, and then the
model learns to predict subjective score
in the second stage. To complement the
inaccuracy of the objective error map
prediction on the homogeneous region,
we also propose a reliability map. Two
simple handcrafted features were additionally employed to further enhance
the accuracy. In addition, we propose a

way to visualize perceptual error maps
to analyze what was learned by the deep
CNN model. In the experiments, the
DIQA yielded the state-of-the-art accuracy on the various databases."
fpgaConvNet: Mapping Regular and
Irregular Convolutional Neural Networks
on FPGAs, by S. I. Venieris and C.-S.
Bouganis, IEEE Transactions on Neural
Networks and Learning Systems,Vol. 30,
No. 2, February 2019, pp. 326-342.
Digital Object Identifier: 10.1109/
TNNLS.2018.2844093
"Since neural networks renaissance,
convolutional neural networks (ConvNets) have demonstrated a state-ofthe-art performance in several emerging
artificial intelligence tasks. The deployment of ConvNets in real-life applications requires power-efficient designs
that meet the application-level performance needs. In this context, field-programmable gate arrays (FPGAs) can

Digital Object Identifier 10.1109/MCI.2019.2919361
Date of publication: 17 July 2019

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2019

provide a potential platform that can be
tailored to application-specific requirements. However, with the complexity of
ConvNet models increasing rapidly, the
ConvNet-to-FPGA design space
becomes prohibitively large. This paper
presents fpgaConvNet, an end-to-end
framework for the optimized mapping
of ConvNets on FPGAs. The proposed
framework comprises an automated
design methodology based on the synchronous dataflow (SDF) paradigm and
defines a set of SDF transformations in
order to efficiently navigate the architectural design space. By proposing a
systematic multiobjective optimization
formulation, the presented framework is
able to generate hardware designs that
are cooptimized for the ConvNet workload, the target device, and the application's performance metric of interest.
Quantitative evaluation shows that the
proposed methodology yields hardware
designs that improve the performance
by up to 6.65× over highly optimized
graphics processing unit designs for the
same power constraints and achieve up
to 2.94× higher performance density
compared with the state-of-the-art
FPGA-based ConvNet architectures."
IEEE Transactions on
Fuzzy Systems

LDS-FCM: A Linear Dynamical System Based Fuzzy C-Means Method for
Tactile Recognition, by C. Liu,
W. Huang, F. Sun, M. Luo, and
C. Tan, IEEE Transactions on Fuzzy
Systems, Vol. 27, No. 1, January 2019,
pp. 72-83.

IEEE Computational Intelligence Magazine - August 2019

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2019