Abstract
We present a new system to optimize feature extraction from 2D-topological data like images in the context of deep learning using correlation among training samples and curriculum learning optimization (CLO). The system treats every sample as 2D random variable, where a pixel contained in the sample is modelled as an independent and identically distributed random variable (i.i.d) realization. With this modelling we utilize information-theoretic and statistical measures of random variables to rank individual training samples and relationship between samples to construct syllabus. The rank of each sample is then used when the sample is fed to the network during training. Comparative evaluation of multiple state-of-the-art networks, including, ResNet, GoogleNet, and VGG, on benchmark datasets demonstrate a syllabus that ranks samples using measures such as Joint Entropy between adjacent samples, can improve learning and significantly reduce the amount of training steps required to achieve desirable training accuracy. We present results that indicate our approach can produce robust feature maps that in turn contribute to reduction of loss by as much as factors of 9 compared to conventional, no-curriculum, training.
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Ghebrechristos, H., Alaghband, G. (2020). Information Theory-Based Curriculum Learning Factory to Optimize Training. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_29
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