Sequence-Dropout Block for Reducing Overfitting Problem in Image Classification

Qian, Ledan; Hu, Libing; Zhao, Li; Wang, Tao; Jiang, Runhua

doi:10.1109/access.2020.2983774

Cited by 34 publications

(12 citation statements)

References 30 publications

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“…The advantages also include efficiency enhancement by hyperparameter per round (of practice). On the other hand, small patches of the previous network caused the improper number of patches, failing to gather good data patches for transfer to the next layers [30,35].…”

Section: Discussionmentioning

confidence: 99%

Scaled Dilation of DropBlock Optimization in Convolutional Neural Network for Fungus Classification

Prommakhot¹,

Srinonchat²

2022

Computers, Materials &Amp; Continua

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Image classification always has open challenges for computer vision research. Nowadays, deep learning has promoted the development of this field, especially in Convolutional Neural Networks (CNNs). This article proposes the development of efficiently scaled dilation of DropBlock optimization in CNNs for the fungus classification, which there are five species in this experiment. The proposed technique adjusts the convolution size at 35, 45, and 60 with the max-polling size 2 × 2. The CNNs models are also designed in 12 models with the different BlockSizes and KeepProp. The proposed techniques provide maximum accuracy of 98.30% for the training set. Moreover, three accurate models, called Precision, Recall, and F1-score, are employed to measure the testing set. The experiment results expose that the proposed models achieve to classify the fungus and provide an excellent accuracy compared with the previous techniques. Furthermore, the proposed techniques can reduce the CNNs structure layer, directly affecting resource and time computation.

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Section: Discussionmentioning

confidence: 99%

Scaled Dilation of DropBlock Optimization in Convolutional Neural Network for Fungus Classification

Prommakhot¹,

Srinonchat²

2022

Computers, Materials &Amp; Continua

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“…Dropout layers were added after each activation function [ 30 ]. The reasons were twofold: to act as a regularizer to prevent overfitting and to measure the model uncertainty.…”

Section: Methodsmentioning

confidence: 99%

Static Attitude Determination Using Convolutional Neural Networks

Santos

Seman

Bezerra

et al. 2021

Sensors

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The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).

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“…We have added regularization techniques in the proposed architecture like dropout, ridge regression, and batch normalization. Dropout helps [20] to prevent overfitting by randomly dropping out (setting to zero) some features (usually hidden units) during training. This forces the model to learn multiple independent representations of the same data, which reduces overfitting and improves generalization.…”

Section: Convolution and Pooling Filtersmentioning

confidence: 99%

A Multi-Modal Deep Learning Approach for Emotion Recognition

Shahzad¹,

Bhatti²,

Jaffar³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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In recent years, research on facial expression recognition (FER) under mask is trending. Wearing a mask for protection from Covid 19 has become a compulsion and it hides the facial expressions that is why FER under the mask is a difficult task. The prevailing unimodal techniques for facial recognition are not up to the mark in terms of good results for the masked face, however, a multimodal technique can be employed to generate better results. We proposed a multimodal methodology based on deep learning for facial recognition under a masked face using facial and vocal expressions. The multimodal has been trained on a facial and vocal dataset. We have used two standard datasets, M-LFW for the masked dataset and CREMA-D and TESS dataset for vocal expressions. The vocal expressions are in the form of audio while the faces data is in image form that is why the data is heterogenous. In order to make the data homogeneous, the voice data is converted into images by taking spectrogram. A spectrogram embeds important features of the voice and it converts the audio format into the images. Later, the dataset is passed to the multimodal for training. neural network and the experimental results demonstrate that the proposed multimodal algorithm outsets unimodal methods and other state-of-the-art deep neural network models.

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Sequence-Dropout Block for Reducing Overfitting Problem in Image Classification

Cited by 34 publications

References 30 publications

Scaled Dilation of DropBlock Optimization in Convolutional Neural Network for Fungus Classification

Scaled Dilation of DropBlock Optimization in Convolutional Neural Network for Fungus Classification

Static Attitude Determination Using Convolutional Neural Networks

A Multi-Modal Deep Learning Approach for Emotion Recognition

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