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EmoDNN: Understanding emotions from short texts through a deep neural network ensemble [article]

Sara Kamran, Raziyeh Zall, Mohammad Reza Kangavari, Saeid Hosseini, Sana Rahmani, Wen Hua
2021 arXiv   pre-print
Emotion recognition from brief contents should embrace the contrast between authors where the differences in personality and cognition can be traced within emotional expressions.  ...  We utilize the outcome vectors in a novel embedding model to foster emotion-pertinent features that are collectively assembled by lexicon inductions.  ...  Deep Neural network approaches [24] [25] [19] [26] surpass traditional methods. Sun et al.  ... 
arXiv:2106.01706v1 fatcat:a4yl4h5jxbfofpilbuzfrbkrdm

Deep Recurrent Semi-Supervised EEG Representation Learning for Emotion Recognition [article]

Guangyi Zhang, Ali Etemad
2021 arXiv   pre-print
EEG-based emotion recognition often requires sufficient labeled training samples to build an effective computational model.  ...  To tackle this problem and reduce the need for output labels in the context of EEG-based emotion recognition, we propose a semi-supervised pipeline to jointly exploit both unlabeled and labeled data for  ...  Deep Neural Network (DNN) has been used to improve the learning process using multiple hidden layers [10] .  ... 
arXiv:2107.13505v1 fatcat:67qlewtmvff3njpjepj2nlea5e

Effective training of convolutional neural networks for age estimation based on knowledge distillation

Antonio Greco, Alessia Saggese, Mario Vento, Vincenzo Vigilante
2021 Neural computing & applications (Print)  
Moreover, in case of age estimation, there is the lack of a large and reliably annotated dataset for training deep neural networks.  ...  Only in recent years, the advent of deep learning allowed for the design of extremely accurate methods based on convolutional neural networks (CNNs) that achieve a remarkable performance in various face  ...  In [10] the authors trained different convolutional neural networks (CNNs) for facial expression recognition with incomplete labeling; they find that the student model often outperforms the teacher on  ... 
doi:10.1007/s00521-021-05981-0 fatcat:b3svtgkgezg4xezibhjx4pdrim

Probabilistic Attribute Tree in Convolutional Neural Networks for Facial Expression Recognition [article]

Jie Cai, Zibo Meng, Ahmed Shehab Khan, Zhiyuan Li, James O'Reilly, and Yan Tong
2018 arXiv   pre-print
More impressively, the PAT-CNN using a single model achieves the best performance for faces in the wild on the SFEW dataset, compared with the state-of-the-art methods using an ensemble of hundreds of  ...  In this paper, we proposed a novel Probabilistic Attribute Tree-CNN (PAT-CNN) to explicitly deal with the large intra-class variations caused by identity-related attributes, e.g., age, race, and gender  ...  We also showed that the proposed soft-clustering with probability outperforms the one based on hard-clustering using the same network structure.  ... 
arXiv:1812.07067v1 fatcat:sdahs47f4ncjhb7lnc6gojoqoi

Multiplane Convolutional Neural Network (Mp-CNN) for Alzheimer's Disease Classification

2022 International Journal of Intelligent Engineering and Systems  
We called this new architecture Multiplane Convolutional Neural Network (Mp-CNN) since it used multiple inputs in its design.  ...  Our method is a new fusion strategy to deal with the disadvantages of shape-based multi-view techniques.  ...  In addition, this study was partially funded by the Education Fund Management Institute (LPDP) under the Innovative Productive Research Grant (RISPRO) scheme -Invitation 2019, contract number: PRJ-41/LPDP  ... 
doi:10.22266/ijies2022.0228.30 fatcat:skhv7yevujhs5iw4tp3xrooede

A convolutional neural network for gender recognition optimizing the accuracy/speed tradeoff

Antonio Greco, Alessia Saggese, Mario Vento, Vincenzo Vigilante
2020 IEEE Access  
The original MobileNet v2 network architecture is marked with the label 224_1.0; this is the largest, most complex model that we experiment and compare with the optimized versions.  ...  DATASETS In this section we are going to introduce the datasets used in our experiments. 1) VGGFace The VGGFace dataset [20] was built to train Deep Neural Networks on the problem of face recognition  ...  ANTONIO GRECO received the Ph.D. in Computer Science and Computer Engineering from the University of Salerno in 2018, where he is currently an Assistant Professor.  ... 
doi:10.1109/access.2020.3008793 fatcat:d6lmavrqtjcdtg4ucrlon5usta

Unsupervised Domain Adaptation for Facial Emotion Recognition in Autistic Children [chapter]

Asha Kurian, Shikha Tripathi
2022 Ambient Intelligence and Smart Environments  
A generalized facial emotion recognition model does not scale well when confronted with the emotions of autistic children due to the domain shift inherent in the distributions of the source (neurotypical  ...  The dearth of labeled datasets in the field of autism exacerbates the problem.  ...  The neural network models are in competition with each other. An improvement in the loss/accuracy of one model comes at the cost of degradation in the other.  ... 
doi:10.3233/aise220022 fatcat:lmwgytllcngzfleqpjfhif7yfe

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
This thesis attacks important problems in the mobile networking area from various perspectives by harnessing recent advances in deep neural networks.  ...  in mobile networks.  ...  Deep neural networks further benefit as training with big data prevents model over-fitting. 3 . Traditional supervised learning is only effective when sufficient labeled data is available.  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

Regional Attention Network (RAN) for Fine-grained Gesture Recognition

Ardhendu Behera, Zachary Wharton, Yonghuai Liu, Morteza Ghahremani, Swagat Kumar, Nik Bessis
2020 IEEE Transactions on Affective Computing  
To this end, we propose a novel end-to-end Regional Attention Network (RAN), which is a fully Convolutional Neural Network (CNN) to combine multiple contextual regions through attention mechanism, focusing  ...  Recent studies on recognition of fine-grained actions/gestures in monocular images have mainly focused on modeling spatial configuration of body parts representing body pose, human-objects interactions  ...  of deep networks [52] .  ... 
doi:10.1109/taffc.2020.3031841 fatcat:7xivqulacbeqhjmdoj2ozmgvba

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking.  ...  In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  Deep neural networks further benefit as training with big data prevents model over-fitting. 3) Traditional supervised learning is only effective when sufficient labeled data is available.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

Applications of Deep Learning and Reinforcement Learning to Biological Data

Mufti Mahmud, Mohammed Shamim Kaiser, Amir Hussain, Stefano Vassanelli
2018 IEEE Transactions on Neural Networks and Learning Systems  
Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize Artificial Intelligence.  ...  This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data.  ...  Neural Network; RF: Random Forest; CNN: Convolutoinal Neural Network; DNN 1 : Deep Neural Network with Genetic Algorithm; DNN 2 : DNN with Cross Entropy; LR: Logistic Regression; SVM: Support Vector  ... 
doi:10.1109/tnnls.2018.2790388 pmid:29771663 fatcat:6r63zihrfvea7cto4ei3mlvqtu

Modeling Subjective Affect Annotations with Multi-Task Learning

Hassan Hayat, Carles Ventura, Agata Lapedriza
2022 Sensors  
The aggregated annotations in emotion modeling may lose the subjective information and actually represent an annotation bias.  ...  For example, emotions experienced while watching a video or evoked by other sources of content, such as news headlines, are subjective: different individuals might perceive or experience different emotions  ...  In the context of addressing emotion subjectivity, Fayek et al. [35] trained multiple ensembles Deep Neural Networks (DNNs), each representing a single annotator.  ... 
doi:10.3390/s22145245 pmid:35890925 pmcid:PMC9319580 fatcat:yckgjzw53jfgdg34wzg5y52rga

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
We first briefly introduce essential background and state-of-theart in deep learning techniques with potential applications to networking.  ...  In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  Deep neural networks further benefit as training with big data prevents model over-fitting. 3) Traditional supervised learning is only effective when sufficient labeled data is available.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

Felix Anda, David Lillis, Aikaterini Kanta, Brett A. Becker, Elias Bou-Harb, Nhien-An Le-Khac, Mark Scanlon
2019 Proceedings of the 14th International Conference on Availability, Reliability and Security - ARES '19  
In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation  ...  Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge.  ...  In further studies, Dong et al. [7] exploited the transfer learning strategy to train deep convolutional neural networks from pretrained models due to the scarcity of age labelled face images.  ... 
doi:10.1145/3339252.3341491 dblp:conf/IEEEares/AndaLKBBLS19 fatcat:bymqsuabejgqvhubidqvsjxwxe

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Hafeez Ur Rehman Siddiqui, Hina Fatima Shahzad, Adil Ali Saleem, Abdul Baqi Khan Khan Khakwani, Furqan Rustam, Ernesto Lee, Imran Ashraf, Sandra Dudley
2021 Sensors  
Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude.  ...  Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc.  ...  The emotion recognition performance of the ensemble models is compared with other machine learning models. The rest of the paper is structured as follows.  ... 
doi:10.3390/s21248336 pmid:34960430 pmcid:PMC8707312 fatcat:izbvdc24mfbcjho32hg66d5mzm
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