762 research outputs found
Neural Class-Specific Regression for face verification
Face verification is a problem approached in the literature mainly using
nonlinear class-specific subspace learning techniques. While it has been shown
that kernel-based Class-Specific Discriminant Analysis is able to provide
excellent performance in small- and medium-scale face verification problems,
its application in today's large-scale problems is difficult due to its
training space and computational requirements. In this paper, generalizing our
previous work on kernel-based class-specific discriminant analysis, we show
that class-specific subspace learning can be cast as a regression problem. This
allows us to derive linear, (reduced) kernel and neural network-based
class-specific discriminant analysis methods using efficient batch and/or
iterative training schemes, suited for large-scale learning problems. We test
the performance of these methods in two datasets describing medium- and
large-scale face verification problems.Comment: 9 pages, 4 figure
On the Dynamics of a Recurrent Hopfield Network
In this research paper novel real/complex valued recurrent Hopfield Neural
Network (RHNN) is proposed. The method of synthesizing the energy landscape of
such a network and the experimental investigation of dynamics of Recurrent
Hopfield Network is discussed. Parallel modes of operation (other than fully
parallel mode) in layered RHNN is proposed. Also, certain potential
applications are proposed.Comment: 6 pages, 6 figures, 1 table, submitted to IJCNN-201
Comprehensive Complexity Assessment of Emerging Learned Image Compression on CPU and GPU
Learned Compression (LC) is the emerging technology for compressing image and
video content, using deep neural networks. Despite being new, LC methods have
already gained a compression efficiency comparable to state-of-the-art image
compression, such as HEVC or even VVC. However, the existing solutions often
require a huge computational complexity, which discourages their adoption in
international standards or products. This paper provides a comprehensive
complexity assessment of several notable methods, that shed light on the
matter, and guide the future development of this field by presenting key
findings. To do so, six existing methods have been evaluated for both encoding
and decoding, on CPU and GPU platforms. Various aspects of complexity such as
the overall complexity, share of each coding module, number of operations,
number of parameters, most demanding GPU kernels, and memory requirements have
been measured and compared on Kodak dataset. The reported results (1) quantify
the complexity of LC methods, (2) fairly compare different methods, and (3) a
major contribution of the work is identifying and quantifying the key factors
affecting the complexity
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple
modalities to a new subspace optimized for one-class classification. The
proposed method iteratively transforms the data from the original feature space
of each modality to a new common feature space along with finding a joint
compact description of data coming from all the modalities. For data in each
modality, we define a separate transformation to map the data from the
corresponding feature space to the new optimized subspace by exploiting the
available information from the class of interest only. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary
material (27 tables, 10 figures). The manuscript and supplementary material
are combined as a single .pdf (50 pages) fil
- …