Deep transform and metric learning network: Wedding deep dictionary learning and neural network

Tang, Wen; Chouzenoux, Émilie; Pesquet, Jean-Christophe; Krim, Hamid

doi:10.1016/j.neucom.2022.08.069

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…The method has improved discriminate power and jointly learns the depth metric and the associated depth transform. We use DeTraMe-Net [74] to ensure the flexibility and anti-interference of the framework structure, and it also has a strong learning ability.…”

Section: Improved Calculation Methodsmentioning

confidence: 99%

A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks

Fan,

Zou

2023

Applied Sciences

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Road crack detection is an important indicator of road detection. In real life, it is very meaningful work to detect road cracks. With the rapid development of science and technology, especially computer science and technology, quite a lot of methods have been applied to crack detection. Traditional detection methods rely on manual identification, which is inefficient and prone to errors. In addition, the commonly used image processing methods are affected by many factors, such as illumination, road stains, etc., so the results are unstable. In the research on pavement crack detection, many research studies mainly focus on the recognition and classification of cracks, lacking the analysis of the specific characteristics of cracks, and the feature values of cracks cannot be measured. Starting from the deep learning method in computer science and technology, this paper proposes a road crack detection technology based on deep learning. It relies on a new deep dictionary learning and encoding network DDLCN, establishes a new activation function MeLU, and adopts a new differentiable calculation method. The technology relies on the traditional Mask-RCNN algorithm and is implemented after improvement. In the comparison of evaluation indicators, the values of recall, precision, and F1-score reflect certain superiority. Experiments show that the proposed method has good implementability and performance in road crack detection and crack feature measurement.

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Section: Improved Calculation Methodsmentioning

confidence: 99%

A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks

Fan,

Zou

2023

Applied Sciences

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“…Similar to pedestrian re-identification [84] and face recognition [85], it is important to learn accurate distance metrics and similarity models in such problems. Therefore, it is desirable to use suitable depth metric learning networks [86] for DM-MOT. Son et al [87] used multiple image blocks as Siamese network inputs to achieve precise localisation by extracting appearance and motion features.…”

Section: Mot With Deep Network Embeddingmentioning

confidence: 99%

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Fei

Han

2023

Sensors

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Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.

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“…We define the solution to the inverse problem (1) as the output of a neural network, whose structure is similar to a recurrent network [23], [24]. Namely, by setting an initial value x 0 , we are interested in the following m-layers neural network with m ∈ N \ {0}:…”

Section: A Unrolled Forward-backward Algorithmmentioning

confidence: 99%

Stability of Unfolded Forward-Backward to Perturbations in Observed Data

de Valle,

Centofanti,

Chouzenoux

et al. 2023

2023 31st European Signal Processing Conference (EUSIPCO)

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We consider a neural network architecture to solve inverse problems, which is built by unfolding a forwardbackward algorithm. This algorithm is based on the minimization of an objective function which corresponds to a penalized least squares problem. In this context, ensuring stability is consistent with inverse problem theory since it guarantees both the continuity of the inversion method and its insensitivity to small noise. The latter is a critical property as deep neural networks have been shown to be vulnerable to adversarial perturbations. The main novelty of our work is to analyze the robustness of this inversion method with respect to a perturbation of the bias parameter of the network. In our architecture, the bias accounts for the observed data in the inverse problem. The analysis is performed by using tools of fixed point theory. Our theoretical results are illustrated by numerical simulations on a problem of signal restoration.

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Deep transform and metric learning network: Wedding deep dictionary learning and neural network

Cited by 4 publications

References 47 publications

A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks

A Novel Road Crack Detection Technology Based on Deep Dictionary Learning and Encoding Networks

Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review

Stability of Unfolded Forward-Backward to Perturbations in Observed Data

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