Master of Science in Computer Science
First Committee Member
Second Committee Member
Third Committee Member
Fourth Committee Member
Number of Pages
We present a method for boosting the performance of the Convolutional Neural Network (CNN) by reducing the covariance between the feature maps of the convolutional layers.
In a CNN, the units of a hidden layer are segmented into the feature/activation maps. The units within a feature map share the weight matrix (filter), or in simple terms look for the same feature. A feature map is the output of one filter applied to the previous layer. CNN search for features such as straight lines, and as these features are spotted, they get reported to the feature map. During the learning process, the convolutional neural network defines what it perceives as important. Each feature map is looking for something else: one feature map could be looking for horizontal lines while the other for vertical lines or curves. Reducing the covariance between the feature maps of a convolutional layer maximizes the variance between the feature maps out of that layer. This supplements the decrement in the redundancy of the feature maps and consequently maximizes the information represented by the feature maps.
Artificial Neural Network; Convolutional Neural Network; Covariance; Data Shadow Tween; Feature maps; Image Classification
University of Nevada, Las Vegas
Basnet, Bikram, "A Novel Feature Maps Covariance Minimization Approach for Advancing Convolutional Neural Network Performance" (2019). UNLV Theses, Dissertations, Professional Papers, and Capstones. 3569.
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