AutoSegNet: An Automated Neural Network for Image Segmentation

Xu, Zhihai; Zuo, Si; Lam, Edmund Y.; Lee, Byoungho; Ni, Chen

doi:10.1109/access.2020.2995367

Cited by 17 publications

(5 citation statements)

References 26 publications

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“…The developed model, also known as AdaEn-Net, has an encoder–decoder structure that automatically adapts to specific image datasets. In the same year, Xu et al [ 108 ] proposed AutoSegNet. Unlike ENAS [ 32 ], which includes five operations, the proposed method uses a small search space but significantly increases search efficiency.…”

Section: Nas For CVmentioning

confidence: 99%

Neural Architecture Search Survey: A Computer Vision Perspective

Kang

Kang²,

Kim

et al. 2023

Sensors

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In recent years, deep learning (DL) has been widely studied using various methods across the globe, especially with respect to training methods and network structures, proving highly effective in a wide range of tasks and applications, including image, speech, and text recognition. One important aspect of this advancement is involved in the effort of designing and upgrading neural architectures, which has been consistently attempted thus far. However, designing such architectures requires the combined knowledge and know-how of experts from each relevant discipline and a series of trial-and-error steps. In this light, automated neural architecture search (NAS) methods are increasingly at the center of attention; this paper aimed at summarizing the basic concepts of NAS while providing an overview of recent studies on the applications of NAS. It is worth noting that most previous survey studies on NAS have been focused on perspectives of hardware or search strategies. To the best knowledge of the present authors, this study is the first to look at NAS from a computer vision perspective. In the present study, computer vision areas were categorized by task, and recent trends found in each study on NAS were analyzed in detail.

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Section: Nas For CVmentioning

confidence: 99%

Neural Architecture Search Survey: A Computer Vision Perspective

Kang

Kang²,

Kim

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A CNN is a multilayer neural network made up of convolutional layers and pooling layers, whose neurons take small patches of the previous layer as input, and fully connected layers. CNNs are currently considered as one of the most popular machine intelligence model for big data analysis in various research areas; a number of CNN architectures with feedback mechanisms applied to image classification and image recognition have been proposed in literature [43], [44], [45], [46], [47].…”

Section: Related Workmentioning

confidence: 99%

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

et al. 2022

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In this paper, we address the problem of automatic image segmentation methods applied to the partial automation of the production process of a traditional Sardinian flatbread called pane Carasau for assuring quality control. The study focuses on one of the most critical activities for obtaining an efficient degree of automation: the estimation of the size and shape of the bread sheets during the production phase, to study the shape variations undergone by the sheet depending on some environmental and production variables. The knowledge can thus be used to create a system capable of predicting the quality of the shape of the dough produced and empower the production process. We implemented an image acquisition system and created an efficient machine learning algorithm, based on support vector machines, for the segmentation and estimation of image measurements for Carasau bread. Experiments demonstrated that the method can successfully achieve accurate segmentation of bread sheets images, ensuring that the dimensions extracted are representative of the sheets coming from the production process. The algorithm proved to be fast and accurate in estimating the size of the bread sheets in various scenarios that occurred over a year of acquisitions. The maximum error committed by the algorithm is equal to the 2.2% of the pixel size in the worst scenario and to 1.2% elsewhere.

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“…Early NAS methods adopt reinforcement learning (RL) or evolutionary strategy [38,2,3,31,30,39] to search among thousands of individually trained networks, which costs huge computation sources. Recent works focus on efficient weight-sharing methods, which falls into two categories: one-shot approaches [6,4,1,7,18,33,29] and gradient-based approaches [32,27,9,8,20,12,34,23], achieve state-of-the-art results on a series of tasks [10,17,24,35,16,28] in various search spaces. They construct a super network/graph which shares weights with all sub-network/graphs.…”

Section: Related Workmentioning

confidence: 99%

Single-DARTS: Towards Stable Architecture Search

Hou¹,

Jin²,

Chen³

2021

Preprint

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Differentiable architecture search (DARTS) marks a milestone in Neural Architecture Search (NAS), boasting simplicity and small search costs. However, DARTS still suffers from frequent performance collapse, which happens when some operations, such as skip connections, zeroes and poolings, dominate the architecture. In this paper, we are the first to point out that the phenomenon is attributed to bi-level optimization.We propose Single-DARTS which merely uses singlelevel optimization, updating network weights and architecture parameters simultaneously with the same data batch. Even single-level optimization has been previously attempted, no literature provides a systematic explanation on this essential point. Replacing the bi-level optimization, Single-DARTS obviously alleviates performance collapse as well as enhances the stability of architecture search. Experiment results show that Single-DARTS achieves state-of-the-art performance on mainstream search spaces. For instance, on NAS-Benchmark-201, the searched architectures are nearly optimal ones. We also validate that the single-level optimization framework is much more stable than the bi-level one. We hope that this simple yet effective method will give some insights on differential architecture search. The code is available at https://github.com/PencilAndBike/Single-DARTS.git.

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AutoSegNet: An Automated Neural Network for Image Segmentation

Cited by 17 publications

References 26 publications

Neural Architecture Search Survey: A Computer Vision Perspective

Neural Architecture Search Survey: A Computer Vision Perspective

A Robust SVM Color-Based Food Segmentation Algorithm for the Production Process of a Traditional Carasau Bread

Single-DARTS: Towards Stable Architecture Search

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