8 research outputs found
A Low-complexity Complex-valued Activation Function for Fast and Accurate Spectral Domain Convolutional Neural Network
Conventional Convolutional Neural Networks (CNNs), which are realized in spatial domain, exhibit high computational complexity. This results in high resource utilization and memory usage and makes them unsuitable for implementation in resource and energy-constrained embedded systems. A promising approach for low-complexity and high-speed solution is to apply CNN modeled in the spectral domain. One of the main challenges in this approach is the design of activation functions. Some of the proposed solutions perform activation functions in spatial domain, necessitating multiple and computationally expensive spatial-spectral domain switching. On the other hand, recent work on spectral activation functions resulted in very computationally intensive solutions. This paper proposes a complex-valued activation function for spectral domain CNNs that only transmits input values that have positive-valued real or imaginary component. This activation function is computationally inexpensive in both forward and backward propagation and provides sufficient nonlinearity that ensures high classification accuracy. We apply this complex-valued activation function in a LeNet-5 architecture and achieve an accuracy gain of up to 7% for MNIST and 6% for Fashion MNIST dataset, while providing up to 79% and 85% faster inference times, respectively, over state-of-the-art activation functions for spectral domain
Graph-Based Analysis and Visualisation of Mobility Data
Urban mobility forecast and analysis can be addressed through grid-based and
graph-based models. However, graph-based representations have the advantage of
more realistically depicting the mobility networks and being more robust since
they allow the implementation of Graph Theory machinery, enhancing the analysis
and visualisation of mobility flows. We define two types of mobility graphs:
Region Adjacency graphs and Origin-Destination graphs. Several node centrality
metrics of graphs are applied to identify the most relevant nodes of the
network in terms of graph connectivity. Additionally, the Perron vector
associated with a strongly connected graph is applied to define a circulation
function on the mobility graph. Such node values are visualised in the
geographically embedded graphs, showing clustering patterns within the network.
Since mobility graphs can be directed or undirected, we define several Graph
Laplacian for both cases and show that these matrices and their spectral
properties provide insightful information for network analysis. The computation
of node centrality metrics and Perron-induced circulation functions for three
different geographical regions demonstrate that basic elements from Graph
Theory applied to mobility networks can lead to structure analysis for graphs
of different connectivity, size, and orientation properties.Comment: 19 pages, 7 figure
Contribution to Graph-based Manifold Learning with Application to Image Categorization.
122 pLos algoritmos de aprendizaje de variedades basados en grafos (Graph,based manifold) son técnicas que han demostrado ser potentes herramientas para la extracción de caracterÃsticas y la reducción de la dimensionalidad en los campos de reconomiento de patrones, visión por computador y aprendizaje automático. Estos algoritmos utilizan información basada en las similitudes de pares de muestras y del grafo ponderado resultante para revelar la estructura geométrica intrÃnseca de la variedad
Contribution to Graph-based Manifold Learning with Application to Image Categorization.
122 pLos algoritmos de aprendizaje de variedades basados en grafos (Graph,based manifold) son técnicas que han demostrado ser potentes herramientas para la extracción de caracterÃsticas y la reducción de la dimensionalidad en los campos de reconomiento de patrones, visión por computador y aprendizaje automático. Estos algoritmos utilizan información basada en las similitudes de pares de muestras y del grafo ponderado resultante para revelar la estructura geométrica intrÃnseca de la variedad
Deep Spectral Convolution Network for Hyperspectral Unmixing
In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures with a deeper network architecture. Second, instead of batch normalization, we propose a spectral normalization layer which improves the selectivity of filters by normalizing their spectral responses. Third, we introduce two fusion configurations that produce ideal abundance maps by using the abstract representations computed from previous layers. In experiments, we use two real datasets to evaluate the performance of our method with other baseline techniques. The experimental results validate that the proposed method outperforms baselines based on Root Mean Square Error (RMSE)