568 research outputs found
Regularized Optimal Transport and the Rot Mover's Distance
This paper presents a unified framework for smooth convex regularization of
discrete optimal transport problems. In this context, the regularized optimal
transport turns out to be equivalent to a matrix nearness problem with respect
to Bregman divergences. Our framework thus naturally generalizes a previously
proposed regularization based on the Boltzmann-Shannon entropy related to the
Kullback-Leibler divergence, and solved with the Sinkhorn-Knopp algorithm. We
call the regularized optimal transport distance the rot mover's distance in
reference to the classical earth mover's distance. We develop two generic
schemes that we respectively call the alternate scaling algorithm and the
non-negative alternate scaling algorithm, to compute efficiently the
regularized optimal plans depending on whether the domain of the regularizer
lies within the non-negative orthant or not. These schemes are based on
Dykstra's algorithm with alternate Bregman projections, and further exploit the
Newton-Raphson method when applied to separable divergences. We enhance the
separable case with a sparse extension to deal with high data dimensions. We
also instantiate our proposed framework and discuss the inherent specificities
for well-known regularizers and statistical divergences in the machine learning
and information geometry communities. Finally, we demonstrate the merits of our
methods with experiments using synthetic data to illustrate the effect of
different regularizers and penalties on the solutions, as well as real-world
data for a pattern recognition application to audio scene classification
Manifold Learning Approaches to Compressing Latent Spaces of Unsupervised Feature Hierarchies
Field robots encounter dynamic unstructured environments containing a vast array of unique objects. In order to make sense of the world in which they are placed, they collect large quantities of unlabelled data with a variety of sensors. Producing robust and reliable applications depends entirely on the ability of the robot to understand the unlabelled data it obtains. Deep Learning techniques have had a high level of success in learning powerful unsupervised representations for a variety of discriminative and generative models. Applying these techniques to problems encountered in field robotics remains a challenging endeavour. Modern Deep Learning methods are typically trained with a substantial labelled dataset, while datasets produced in a field robotics context contain limited labelled training data. The primary motivation for this thesis stems from the problem of applying large scale Deep Learning models to field robotics datasets that are label poor. While the lack of labelled ground truth data drives the desire for unsupervised methods, the need for improving the model scaling is driven by two factors, performance and computational requirements. When utilising unsupervised layer outputs as representations for classification, the classification performance increases with layer size. Scaling up models with multiple large layers of features is problematic, as the sizes of subsequent hidden layers scales with the size of the previous layer. This quadratic scaling, and the associated time required to train such networks has prevented adoption of large Deep Learning models beyond cluster computing. The contributions in this thesis are developed from the observation that parameters or filter el- ements learnt in Deep Learning systems are typically highly structured, and contain related ele- ments. Firstly, the structure of unsupervised filters is utilised to construct a mapping from the high dimensional filter space to a low dimensional manifold. This creates a significantly smaller repre- sentation for subsequent feature learning. This mapping, and its effect on the resulting encodings, highlights the need for the ability to learn highly overcomplete sets of convolutional features. Driven by this need, the unsupervised pretraining of Deep Convolutional Networks is developed to include a number of modern training and regularisation methods. These pretrained models are then used to provide initialisations for supervised convolutional models trained on low quantities of labelled data. By utilising pretraining, a significant increase in classification performance on a number of publicly available datasets is achieved. In order to apply these techniques to outdoor 3D Laser Illuminated Detection And Ranging data, we develop a set of resampling techniques to provide uniform input to Deep Learning models. The features learnt in these systems outperform the high effort hand engineered features developed specifically for 3D data. The representation of a given signal is then reinterpreted as a combination of modes that exist on the learnt low dimensional filter manifold. From this, we develop an encoding technique that allows the high dimensional layer output to be represented as a combination of low dimensional components. This allows the growth of subsequent layers to only be dependent on the intrinsic dimensionality of the filter manifold and not the number of elements contained in the previous layer. Finally, the resulting unsupervised convolutional model, the encoding frameworks and the em- bedding methodology are used to produce a new unsupervised learning stratergy that is able to encode images in terms of overcomplete filter spaces, without producing an explosion in the size of the intermediate parameter spaces. This model produces classification results on par with state of the art models, yet requires significantly less computational resources and is suitable for use in the constrained computation environment of a field robot
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
Sparse Modeling for Image and Vision Processing
In recent years, a large amount of multi-disciplinary research has been
conducted on sparse models and their applications. In statistics and machine
learning, the sparsity principle is used to perform model selection---that is,
automatically selecting a simple model among a large collection of them. In
signal processing, sparse coding consists of representing data with linear
combinations of a few dictionary elements. Subsequently, the corresponding
tools have been widely adopted by several scientific communities such as
neuroscience, bioinformatics, or computer vision. The goal of this monograph is
to offer a self-contained view of sparse modeling for visual recognition and
image processing. More specifically, we focus on applications where the
dictionary is learned and adapted to data, yielding a compact representation
that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics
and Visio
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
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