Efficient Diverse Ensemble for Discriminative Co-tracking

Meshgi, Kourosh; Oba, Shigeyuki; Ishii, Shin

doi:10.1109/cvpr.2018.00506

Cited by 28 publications

(10 citation statements)

References 55 publications

(116 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make this training-intensive technology accessible to everyone, recent studies have explored ways to reduce redundancy in ensembling. Meshgi, Oba, and Ishii (2018) have exploited concepts from active learning to reduce training time and redundancy. Rather than using the whole dataset for training, their ensemble method is trained on the most informative samples that maximize learning based on the query by committee algorithm (Seung, Opper, and Sompolinsky 1992).…”

Section: Introductionmentioning

confidence: 99%

Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

Siqueira

Magg

Wermter

2020

AAAI

125

View full text Add to dashboard Cite

Ensemble methods, traditionally built with independently trained de-correlated models, have proven to be efficient methods for reducing the remaining residual generalization error, which results in robust and accurate methods for real-world applications. In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient. In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions. We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR without loss of diversity and generalization power, which are both important for ensemble performance. Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.

show abstract

Section: Introductionmentioning

confidence: 99%

Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

Siqueira

Magg

Wermter

2020

AAAI

125

View full text Add to dashboard Cite

show abstract

“…ensembling. Meshgi, Oba, and Ishii (2018) have exploited concepts from active learning to reduce training time and redundancy. Rather than using the whole dataset for training, their ensemble method is trained on the most informative samples that maximize learning based on the query by committee algorithm (Seung, Opper, and Sompolinsky 1992).…”

Section: Introductionmentioning

confidence: 99%

Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks

Siqueira¹,

Magg²,

Wermter³

2020

Preprint

View full text Add to dashboard Cite

Ensemble methods, traditionally built with independently trained de-correlated models, have proven to be efficient methods for reducing the remaining residual generalization error, which results in robust and accurate methods for realworld applications. In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient. In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions. We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR without loss of diversity and generalization power, which are both important for ensemble performance. Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.

show abstract

“…Meshgi et al used the active learning method to reduce training time and redundancy. Meshgi did not use the entire data set, but used the most useful data for training [46,47]. In addition to active learning, the input space is decomposed into multiple regions, and the divide and conquer strategy of training a convolutional neural network in each region can also reduce redundancy.…”

Section: Esrs (Ensembles With Shared Representations)mentioning

confidence: 99%

HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

Zhong

Sun

et al. 2021

Symmetry

View full text Add to dashboard Cite

As we all know, there are many ways to express emotions. Among them, facial emotion recognition, which is widely used in human–computer interaction, psychoanalysis of mental patients, multimedia retrieval, and other fields, is still a challenging task. At present, although convolutional neural network has achieved great success in face emotion recognition algorithms, it has a rising space in effective feature extraction and recognition accuracy. According to a large number of literature studies, histogram of oriented gradient (HOG) can effectively extract face features, and ensemble methods can effectively improve the accuracy and robustness of the algorithm. Therefore, this paper proposes a new algorithm, HOG-ESRs, which improves the traditional ensemble methods to the ensembles with shared representations (ESRs) method, effectively reducing the residual generalization error, and then combining HOG features with ESRs. The experimental results on the FER2013 dataset show that the new algorithm can not only effectively extract features and reduce the residual generalization error, but also improve the accuracy and robustness of the algorithm, the purpose of the study being achieved. The application of HOG-ESRs in facial emotion recognition is helpful to solve the symmetry of edge detection and the deficiency of related methods in an outdoor lighting environment.

show abstract

Efficient Diverse Ensemble for Discriminative Co-tracking

Cited by 28 publications

References 55 publications

Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks

HOG-ESRs Face Emotion Recognition Algorithm Based on HOG Feature and ESRs Method

Contact Info

Product

Resources

About