Understanding How Image Quality Affects Deep Neural Networks

Image quality is an important practical challenge that is often overlooked in the design of machine vision systems. Commonly, machine vision systems are trained and tested on high quality image datasets, yet in practical applications the input images can not be assumed to be of high quality. Recently, deep neural networks have obtained state-of-the-art performance on many machine vision tasks. In this paper we provide an evaluation of 4 state-of-the-art deep neural network models for image classification under quality distortions. We consider five types of quality distortions: blur, noise, contrast, JPEG, and JPEG2000 compression. We show that the existing networks are susceptible to these quality distortions, particularly to blur and noise. These results enable future work in developing deep neural networks that are more invariant to quality distortions.Comment: Final version will appear in IEEE Xplore in the Proceedings of the Conference on the Quality of Multimedia Experience (QoMEX), June 6-8, 201

Expert Selection in High-Dimensional Markov Decision Processes

In this work we present a multi-armed bandit framework for online expert selection in Markov decision processes and demonstrate its use in high-dimensional settings. Our method takes a set of candidate expert policies and switches between them to rapidly identify the best performing expert using a variant of the classical upper confidence bound algorithm, thus ensuring low regret in the overall performance of the system. This is useful in applications where several expert policies may be available, and one needs to be selected at run-time for the underlying environment.Comment: In proceedings of the 59th IEEE Conference on Decision and Control 2020. arXiv admin note: text overlap with arXiv:1707.0571

A Novel Deep Belief Network Architecture with Interval Type-2 Fuzzy Set Based Uncertain Parameters Towards Enhanced Learning

This paper proposes a novel Deep Belief Network (DBN) architecture, the ‘Interval Type-2 Fuzzy DBN (IT2FDBN)’, which models the weights and biases with IT2 FSs. Thus, it introduces a novel algorithm for augmented deep leaning, which has the capability to address all the limitations of the classical DBN (CDBN) and T1 fuzzy DBN (T1FDBN). We comparatively evaluate the performance of the IT2FDBN by conducting experiments using the popular MNIST handwritten digit recognition datasets. Additionally, to demonstrate its robustness and generalization capabilities, we also conduct experiments taking two noisy variants of MNIST dataset, viz. the MNIST with AWGN (additive white Gaussian noise) and the MNIST with motion blur. We conduct extensive simulations by considering different combinations of nodes in the hidden layers of the DBN for better model selection. We thoroughly compare the results using well-known performance measures such as root mean square error (RMSE) and Error rate. We show that, in terms of RMSE values and error rates, the proposed IT2FDBN outperforms both T1FDBN and CDBN across all the three datasets. Further, we also provide the results of convergence, runtime-based comparison, and statistical analysis in support of our proposal.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Estudo dos efeitos de imagens degradadas no processo de reconhecimento de objetos por redes neurais convolucionais

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Curso de Graduação em Engenharia de Controle e Automação, 2017.Decorrente da constante evolução tecnológica e da crescente disponibilidade de dados, em especial imagens, cresce a utilização de abordagens de programação orientada a dados, em que se destacam as aplicações de aprendizagem profunda de máquinas utilizando redes neurais convolucionais. Porém a alta qualidade das imagens fornecidas para treinamento ou comumente para avaliação, contrasta com a qualidade de imagens cotidianas ou de aplicações específicas, o que leva ao questionamento dos efeitos deste tipo de situação encontrada e o seu impacto no processo de classificação realizado pela rede neural convolucional. Para contemplar e responder estes questionamentos, neste trabalho foram avaliadas cinco modelos do estado-da-arte de redes convolucionais treinadas, sujeitas à sete tipos de degradação de qualidade, mostrando a sensibilidade a este conjunto de degradações e as influências na robustez do processo de classificação de objetos em imagens.Due to constant technological evolution and the increasing availability of data, in particular images, the use of data-driven programming approaches is growing, in which stands out the applications of deep machine learning using convolutional neural networks. However, the high quality of the images provided for training or commonly for evaluation, contrasts with the quality of everyday images or of specific applications, which leads to the questioning of the effects of this kind of faced situation and its impact on the classification process performed by the convolutional neural network. In order to contemplate and answer these questions, this document evaluated five stateof-the-art models of trained convolutional networks, subject to seven types of quality degradations, showing sensitivity to this set of degradations and the influences on the robustness of the object classification process on pictures