Go Deep or Go Home?
M.C. den Heijer (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Tom Julian Viering – Mentor (TU Delft - Computer Science & Engineering-Teaching Team)
Y. Kato – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
O.T. Turan – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Ziqi Wang – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
M Loog – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
David Tax – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that going deeper does not guarantee an improvement in performance.