A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, imagerelated tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research

Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.Comment: 20 pages (double column), 2 figures, 3 tables, 157 reference

Explaining Deep Convolutional Neural Networks for Image Classification by Evolving Local Interpretable Model-agnostic Explanations

Deep convolutional neural networks have proven their effectiveness, and have been acknowledged as the most dominant method for image classification. However, a severe drawback of deep convolutional neural networks is poor explainability. Unfortunately, in many real-world applications, users need to understand the rationale behind the predictions of deep convolutional neural networks when determining whether they should trust the predictions or not. To resolve this issue, a novel genetic algorithm-based method is proposed for the first time to automatically evolve local explanations that can assist users to assess the rationality of the predictions. Furthermore, the proposed method is model-agnostic, i.e., it can be utilised to explain any deep convolutional neural network models. In the experiments, ResNet is used as an example model to be explained, and the ImageNet dataset is selected as the benchmark dataset. DenseNet and MobileNet are further explained to demonstrate the model-agnostic characteristic of the proposed method. The evolved local explanations on four images, randomly selected from ImageNet, are presented, which show that the evolved local explanations are straightforward to be recognised by humans. Moreover, the evolved explanations can explain the predictions of deep convolutional neural networks on all four images very well by successfully capturing meaningful interpretable features of the sample images. Further analysis based on the 30 runs of the experiments exhibits that the evolved local explanations can also improve the probabilities/confidences of the deep convolutional neural network models in making the predictions. The proposed method can obtain local explanations within one minute, which is more than ten times faster than LIME (the state-of-the-art method)

A Survey on Surrogate-assisted Efficient Neural Architecture Search

Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users without rich expertise to benefit from the success of deep neural networks (DNNs). However, NAS is still laborious and time-consuming because a large number of performance estimations are required during the search process of NAS, and training DNNs is computationally intensive. To solve the major limitation of NAS, improving the efficiency of NAS is essential in the design of NAS. This paper begins with a brief introduction to the general framework of NAS. Then, the methods for evaluating network candidates under the proxy metrics are systematically discussed. This is followed by a description of surrogate-assisted NAS, which is divided into three different categories, namely Bayesian optimization for NAS, surrogate-assisted evolutionary algorithms for NAS, and MOP for NAS. Finally, remaining challenges and open research questions are discussed, and promising research topics are suggested in this emerging field.Comment: 18 pages, 7 figure

Efficient evolutionary-based neural architecture search in few GPU hours for image classification and medical image segmentation

Orientador: Lucas Ferrari de OliveiraTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 20/09/2021Inclui referências: p. 132-139Área de concentração: Ciência da ComputaçãoResumo: O uso de aprendizagem profunda (AP) está crescendo rapidamente, já que o poder computacional atual fornece otimização e inferência rápidas. Além disso, vários métodos exclusivos de AP estão evoluindo, permitindo resultados superiores em visão computacional, reconhecimento de voz e análise de texto. Os métodos AP extraem característica automaticamente para melhor representação de um problema específico, removendo o árduo trabalho do desenvolvimento de descritores de características dos métodos convencionais. Mesmo que esse processo sejaautomatizado, a criação inteligente de redes neurais é necessária para o aprendizado adequado da representação, o que requer conhecimento em AP. O campo de busca de arquiteturas neurais (BAN) foca no desenvolvimento de abordagens inteligentes que projetam redes robustas automaticamente para reduzir o conhecimento exigido para o desenvolvimento de redes eficientes. BAN pode fornecer maneiras de descobrir diferentes representações de rede, melhorando o estado da arte em diferentes aplicações. Embora BAN seja relativamente nova, várias abordagens foram desenvolvidas para descobrir modelos robustos. Métodos eficientes baseados em evolução são amplamente populares em BAN, mas seu alto consumo de placa gráfica (de alguns dias a meses)desencoraja o uso prático. No presente trabalho, propomos duas abordagens BAN baseadas na evolução eficiente com baixo custo de processamento, exigindo apenas algumas horas de processamento na placa gráfica (menos de doze em uma RTX 2080Ti) para descobrir modelos competitivos. Nossas abordagens extraem conceitos da programação de expressão gênica para representar e gerar redes baseadas em células robustas combinadas com rápido treinamento de candidatos, compartilhamento de peso e combinações dinâmicas. Além disso, os métodos propostos são empregados em um espaço de busca mais amplo, com mais células representando uma rede única. Nossa hipótese central é que BAN baseado na evolução pode ser usado em uma busca com baixo custo (combinada com uma estratégia robusta e busca eficiente) em diversas tarefas de visão computacional sem perder competitividade. Nossos métodos são avaliados em diferentes problemas para validar nossa hipótese: classificação de imagens e segmentação semântica de imagens médicas. Para tanto, as bases de dados CIFAR são estudadas para atarefa de classificação e o desafio CHAOS para a tarefa de segmentação. As menores taxas de erro encontradas nas bases CIFAR-10 e CIFAR-100 foram 2,17% ± 0,10 e 15,47% ± 0,51,respectivamente. Quanto às tarefas do desafio CHAOS, os valores de Dice ficaram entre 90% e96%. Os resultados obtidos com nossas propostas em ambas as tarefas mostraram a descoberta de redes robustas para ambas as tarefas com baixo custo na fase de busca, sendo competitivas em relação ao estado da arte em ambos os desafios.Abstract: Deep learning (DL) usage is growing fast since current computational power provides fast optimization and inference. Furthermore, several unique DL methods are evolving, enabling superior computer vision, speech recognition, and text analysis results. DL methods automatically extract features to represent a specific problem better, removing the hardworking of feature engineering from conventional methods. Even if this process is automated, intelligent network design is necessary for proper representation learning, which requires expertise in DL. The neural architecture search (NAS) field focuses on developing intelligent approaches that automatically design robust networks to reduce the expertise required for developing efficient networks. NAS may provide ways to discover different network representations, improving the state-of-the-art indifferent applications. Although NAS is relatively new, several approaches were developed for discovering robust models. Efficient evolutionary-based methods are widely popular in NAS, buttheir high GPU consumption (from a few days to months) discourages practical use. In the presentwork, we propose two efficient evolutionary-based NAS approaches with low-GPU cost, requiring only a few GPU hours (less than twelve in an RTX 2080Ti) to discover competitive models. Our approaches extract concepts from gene expression programming to represent and generate robust cell-based networks combined with fast candidate training, weight sharing, and dynamic combinations. Furthermore, the proposed methods are employed in a broader search space, withmore cells representing a unique network. Our central hypothesis is that evolutionary-based NAScan be used in a low-cost GPU search (combined with a robust strategy and efficient search) indiverse computer vision tasks without losing competitiveness. Our methods are evaluated indifferent problems to validate our hypothesis: image classification and medical image semantic segmentation. For this purpose, the CIFAR datasets are studied for the classification task andthe CHAOS challenge for the segmentation task. The lowest error rates found in CIFAR-10 andCIFAR-100 datasets were 2.17% ± 0.10 and 15.47% ± 0.51, respectively. As for the CHAOS challenge tasks, the dice scores were between 90% and 96%. The obtained results from our proposal in both tasks shown the discovery of robust networks for both tasks with little GPU costin the search phase, being competitive to state-of-the-art approaches in both challenges

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp