IGWO-SS: Improved Grey Wolf Optimization Based on Synaptic Saliency for Fast Neural Architecture Search in Computer Vision

Arman, Shifat E; Deowan, Shamim Ahmed

doi:10.1109/access.2022.3184291

Cited by 7 publications

(2 citation statements)

References 60 publications

(91 reference statements)

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“…Designing optimal CNN architectures for specific datasets has traditionally demanded substantial manual effort. Recent advancements in network architecture design have introduced three main approaches to automate this process: reinforcement learning-based [14][15][16], gradient-based [17,18], and metaheuristic search algorithm (MSA)-based [19,20] methods.…”

Section: Recent Progress In Network Architecture Design Techniquesmentioning

confidence: 99%

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Ang,

Lim,

Tiang

et al. 2023

Mathematics

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Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

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Section: Recent Progress In Network Architecture Design Techniquesmentioning

confidence: 99%

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Ang,

Lim,

Tiang

et al. 2023

Mathematics

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“…This manual process can be laborious and time-consuming. Advancements in automated network architecture design have given rise to four primary approaches: reinforcement learning (RL)-based [13][14][15], gradient descent (GD)-based [16], Bayesian optimization (BO)-based [17][18][19], and metaheuristic search algorithm (MSA)-based [20][21][22] methods.…”

Section: Recent Advances In Automated Network Architecture Designmentioning

confidence: 99%

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning

Ang,

Lim,

Tiang

et al. 2023

Biomimetics

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This study introduces ETLBOCBL-CNN, an automated approach for optimizing convolutional neural network (CNN) architectures to address classification tasks of varying complexities. ETLBOCBL-CNN employs an effective encoding scheme to optimize network and learning hyperparameters, enabling the discovery of innovative CNN structures. To enhance the search process, it incorporates a competency-based learning concept inspired by mixed-ability classrooms during the teacher phase. This categorizes learners into competency-based groups, guiding each learner’s search process by utilizing the knowledge of the predominant peers, the teacher solution, and the population mean. This approach fosters diversity within the population and promotes the discovery of innovative network architectures. During the learner phase, ETLBOCBL-CNN integrates a stochastic peer interaction scheme that encourages collaborative learning among learners, enhancing the optimization of CNN architectures. To preserve valuable network information and promote long-term population quality improvement, ETLBOCBL-CNN introduces a tri-criterion selection scheme that considers fitness, diversity, and learners’ improvement rates. The performance of ETLBOCBL-CNN is evaluated on nine different image datasets and compared to state-of-the-art methods. Notably, ELTLBOCBL-CNN achieves outstanding accuracies on various datasets, including MNIST (99.72%), MNIST-RD (96.67%), MNIST-RB (98.28%), MNIST-BI (97.22%), MNST-RD + BI (83.45%), Rectangles (99.99%), Rectangles-I (97.41%), Convex (98.35%), and MNIST-Fashion (93.70%). These results highlight the remarkable classification accuracy of ETLBOCBL-CNN, underscoring its potential for advancing smart device infrastructure development.

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Improved Grasshopper Optimization with Squeezenet (IGO-SNet) Classifier for Banana Leaf Diseases

Rubadevi,

Divya

2024

Lecture Notes in Networks and Systems

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IGWO-SS: Improved Grey Wolf Optimization Based on Synaptic Saliency for Fast Neural Architecture Search in Computer Vision

Cited by 7 publications

References 60 publications

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Optimizing Image Classification: Automated Deep Learning Architecture Crafting with Network and Learning Hyperparameter Tuning

Improved Grasshopper Optimization with Squeezenet (IGO-SNet) Classifier for Banana Leaf Diseases

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