Automatic Feature Learning for Robust Shadow Detection

2014 ◽  
Author(s):  
Salman Hameed Khan ◽  
Mohammed Bennamoun ◽  
Ferdous Sohel ◽  
Roberto Togneri
Keyword(s):  
Feature Learning ◽  
Shadow Detection

Instruction Helps and Hinders Young Children’s Feature Learning

2018 ◽  
Author(s):  
Charles Kalish ◽  
Nigel Noll
Keyword(s):  
Young Children ◽  
Feature Learning ◽  
Irrelevant Information ◽  
Diagnostic Feature ◽  
Older Children ◽  
Prediction Task ◽  
Feature Correlation ◽  
The Future

Existing research suggests that adults and older children experience a tradeoff where instruction and feedback help them solve a problem efficiently, but lead them to ignore currently irrelevant information that might be useful in the future. It is unclear whether young children experience the same tradeoff. Eighty-seven children (ages five- to eight-years) and 42 adults participated in supervised feature prediction tasks either with or without an instructional hint. Follow-up tasks assessed learning of feature correlations and feature frequencies. Younger children tended to learn frequencies of both relevant and irrelevant features without instruction, but not the diagnostic feature correlation needed for the prediction task. With instruction, younger children did learn the diagnostic feature correlation, but then failed to learn the frequencies of irrelevant features. Instruction helped older children learn the correlation without limiting attention to frequencies. Adults learned the diagnostic correlation even without instruction, but with instruction no longer learned about irrelevant frequencies. These results indicate that young children do show some costs of learning with instruction characteristic of older children and adults. However, they also receive some of the benefits. The current study illustrates just what those tradeoffs might be, and how they might change over development.

Construction of a deep sparse filtering network for rotating machinery fault diagnosis

2021 ◽  
pp. 095440702110148
Author(s):  
Chun Cheng ◽  
Wei Zou ◽  
Weiping Wang ◽  
Michael Pecht
Keyword(s):  
Fault Diagnosis ◽  
Back Propagation ◽  
Feature Learning ◽  
Back Propagation Neural Network ◽  
Rotating Machinery ◽  
Fine Tuning ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Intelligent Fault Diagnosis ◽  
Sparse Filtering

Deep neural networks (DNNs) have shown potential in intelligent fault diagnosis of rotating machinery. However, traditional DNNs such as the back-propagation neural network are highly sensitive to the initial weights and easily fall into the local optimum, which restricts the feature learning capability and diagnostic performance. To overcome the above problems, a deep sparse filtering network (DSFN) constructed by stacked sparse filtering is developed in this paper and applied to fault diagnosis. The developed DSFN is pre-trained by sparse filtering in an unsupervised way. The back-propagation algorithm is employed to optimize the DSFN after pre-training. Then, the DSFN-based intelligent fault diagnosis method is validated using two experiments. The results show that pre-training with sparse filtering and fine-tuning can help the DSFN search for the optimal network parameters, and the DSFN can learn discriminative features adaptively from rotating machinery datasets. Compared with classical methods, the developed diagnostic method can diagnose rotating machinery faults with higher accuracy using fewer training samples.

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