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Using curriculum learning to improve the performance of deep learning models used for classification purposes

Molenaar, R.S.J. (2021) Using curriculum learning to improve the performance of deep learning models used for classification purposes.

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Abstract:The current growth of neural networks means that the importance of the speed and accuracy of these neural networks, especially during training, is becoming more and more important. Although the construction of Convolutional Neural Networks has grown a lot, with pruning nodes, removing completely linked layers, and cutting down on filters, the training processes of these neural networks leaves a lot to be desired. This paper focuses on increasing the training performance and speed of neural networks using a technique called curriculum learning. This method was formulated to represent the manner in which humans learn, starting with easier concepts and following it up with harder ones. To use this strategy, a curriculum must be made based on a metric. In this research, this metric was chosen to be the Age of Acquisition for words, ranking concepts on at what age humans learn words. Both easy and hard AoA classes were tested on accuracy and training perfor�mance, together with multiple test as a baseline. The results show a significant improvement, but further research must be done to confirm it and to explore this idea further.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Creative Technology BSc (50447)
Link to this item:http://purl.utwente.nl/essays/87958
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