Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Jiang, Weiwei; Zhang, Le

doi:10.1587/transinf.2019edl8199

Cited by 60 publications

(18 citation statements)

References 15 publications

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“…There are many deep learning models [6] that aid in identifying a masked face. In [7], the aim is to develop a deep learning model for detecting unmasked people.…”

Section: Masked Face Detectionmentioning

confidence: 99%

Masked Face Detection and Calibration with Deep Learning Models

2022

J. Phys.: Conf. Ser.

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Under the COVID-19 pandemic, the demand that face detection devices should be enhanced to detect masked faces is imperative. In this study, we utilize several state-of-the-art face detection models and compare them on various unmasked and masked human face datasets. Moreover, by analyzing the results we obtain, we evaluate these disparate models and discover some problems. Attempting to overcome the problems discovered, we propose and implement several improvements, and acquire more results for analysis. At length, we propose some ideas for future research directions.

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“…There are many deep learning models [6] that aid in identifying a masked face. In [7], the aim is to develop a deep learning model for detecting unmasked people.…”

Section: Masked Face Detectionmentioning

confidence: 99%

Masked Face Detection and Calibration with Deep Learning Models

2022

J. Phys.: Conf. Ser.

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“…Python 3.7 is chosen as the programming language for implementing both the machine learning and deep learning models, because of its popularity and wide support in the machine learning community. Specifically speaking, the machine learning models are implemented with the packages including scikitlearn 11 , xgboost 12 , lightgbm 13 , and catboost 14 . The deep learning models are implemented with the packages including pytorch 15 , fastai 16 , and tsai 17 .…”

Section: Settingsmentioning

confidence: 99%

“…However, machine learning models usually require a lot of data to work, which is not a serious problem in the environmental area with more and more observations accumulated in past decades. As a specific type of machine learning, deep learning is represented by various deep neural networks, which have been extremely successfully in the past decade for a series of problems, e.g., image recognition, time series prediction, etc [12,13,14]. Their effectiveness for drought prediction would also be evaluated in this study, along with the traditional machine learning models.…”

Section: Introductionmentioning

confidence: 99%

An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

Jiang,

Luo

2021

Preprint

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Drought is a serious natural disaster that has a long duration and a wide range of influence. To decrease the drought-caused losses, drought prediction is the basis of making the corresponding drought prevention and disaster reduction measures. While this problem has been studied in the literature, it remains unknown whether drought can be precisely predicted or not with machine learning models using weather data. To answer this question, a real-world public dataset is leveraged in this study and different drought levels are predicted using the last 90 days of 18 meteorological indicators as the predictors. In a comprehensive approach, 16 machine learning models and 16 deep learning models are evaluated and compared. The results show no single model can achieve the best performance for all evaluation metrics simultaneously, which indicates the drought prediction problem is still challenging. As benchmarks for further studies, the code and results are publicly available in a Github repository.

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“…Recently, with the rise of big data, cloud computing and GPU-based acceleration, deep neural networks, including convolutional neural networks and residual networks, have achieved a great success for many problems, e.g., image classification [23], object detection [38], traffic forecasting [25], and handwritten numeral recognition [26]. Neural networks have also been applied to time series classification, e.g., a multi-channel CNN (MC-CNN) is proposed for multivariate time series classification [45], and a multiscale CNN approach is proposed for univariate time series classification [12].…”

Section: Electronic Supplementary Materialsmentioning

confidence: 99%

Time series classification: nearest neighbor versus deep learning models

Jiang

2020

SN Appl. Sci.

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Time series classification has been an important and challenging research task. In different domains, time series show different patterns, which makes it difficult to design a global optimal solution and requires a comprehensive evaluation of different classifiers across multiple datasets. With the rise of big data and cloud computing, deep learning models, especially deep neural networks, arise as a new paradigm for many problems, including image classification, object detection and natural language processing. In recent years, deep learning models are also applied for time series classification and show superiority over traditional models. However, the previous evaluation is usually limited to a small number of datasets and lack of significance analysis. In this study, we give a comprehensive comparison between nearest neighbor and deep learning models. Specifically, we compare 1-NN classifiers with eight different distance measures and three state-of-the-art deep learning models on 128 time series datasets. Our results indicate that deep learning models are not significantly better than 1-NN classifiers with edit distance with real penalty and dynamic time warping.

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Edge-SiamNet and Edge-TripleNet: New Deep Learning Models for Handwritten Numeral Recognition

Cited by 60 publications

References 15 publications

Masked Face Detection and Calibration with Deep Learning Models

Masked Face Detection and Calibration with Deep Learning Models

An Evaluation of Machine Learning and Deep Learning Models for Drought Prediction using Weather Data

Time series classification: nearest neighbor versus deep learning models

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