37 research outputs found
An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts
In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons
On the Usage of GPUs for Efficient Motion Estimation in Medical Image Sequences
Images are ubiquitous in biomedical applications from basic research to clinical practice. With the rapid increase in resolution, dimensionality of the images and the need for real-time performance in many applications, computational requirements demand proper exploitation of multicore architectures. Towards this, GPU-specific implementations of image analysis algorithms are particularly promising. In this paper, we investigate the mapping of an enhanced motion estimation algorithm to novel GPU-specific architectures, the resulting challenges and benefits therein. Using a database of three-dimensional image sequences, we show that the mapping leads to substantial performance gains, up to a factor of 60, and can provide near-real-time experience. We also show how architectural peculiarities of these devices can be best exploited in the benefit of algorithms, most specifically for addressing the challenges related to their access patterns and different memory configurations. Finally, we evaluate the performance of the algorithm on three different GPU architectures and perform a comprehensive analysis of the results
A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
Markov Chain Monte Carlo (MCMC) is a well-established family of algorithms which are primarily used in Bayesian statistics to sample from a target distribution when direct sampling is challenging. Single instances of MCMC methods are widely considered hard to parallelise in a problem-agnostic fashion and hence, unsuitable to meet both constraints of high accuracy and high throughput. Sequential Monte Carlo (SMC) Samplers can address the same problem, but are parallelisable: they share with Particle Filters the same key tasks and bottleneck. Although a rich literature already exists on MCMC methods, SMC Samplers are relatively underexplored, such that no parallel implementation is currently available. In this paper, we first propose a parallel MPI version of the SMC Sampler, including an optimised implementation of the bottleneck, and then compare it with single-core Metropolis-Hastings. The goal is to show that SMC Samplers may be a promising alternative to MCMC methods with high potential for future improvements. We demonstrate that a basic SMC Sampler with 512 cores is up to 85 times faster or up to 8 times more accurate than Metropolis-Hastings
Energy Efficiency Fairness Beamforming Designs for MISO NOMA Systems
In this paper, we propose two beamforming designs for a multiple-input
single-output non-orthogonal multiple access system considering the energy
efficiency (EE) fairness between users. In particular, two quantitative
fairness-based designs are developed to maintain fairness between the users in
terms of achieved EE: max-min energy efficiency (MMEE) and proportional
fairness (PF) designs. While the MMEE-based design aims to maximize the minimum
EE of the users in the system, the PF-based design aims to seek a good balance
between the global energy efficiency of the system and the EE fairness between
the users. Detailed simulation results indicate that our proposed designs offer
many-fold EE improvements over the existing energy-efficient beamforming
designs.Comment: IEEE WCNC 201
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning
Correlation filter has been proven to be an effective tool for a number of
approaches in visual tracking, particularly for seeking a good balance between
tracking accuracy and speed. However, correlation filter based models are
susceptible to wrong updates stemming from inaccurate tracking results. To
date, little effort has been devoted towards handling the correlation filter
update problem. In this paper, we propose a novel approach to address the
correlation filter update problem. In our approach, we update and maintain
multiple correlation filter models in parallel, and we use deep reinforcement
learning for the selection of an optimal correlation filter model among them.
To facilitate the decision process in an efficient manner, we propose a
decision-net to deal target appearance modeling, which is trained through
hundreds of challenging videos using proximal policy optimization and a
lightweight learning network. An exhaustive evaluation of the proposed approach
on the OTB100 and OTB2013 benchmarks show that the approach is effective enough
to achieve the average success rate of 62.3% and the average precision score of
81.2%, both exceeding the performance of traditional correlation filter based
trackers.Comment: 13 pages, 11 figure