Abstract
The performance of all types of neural networks is affected by initialization of weights since most of these networks follow some form of the derivative gradient descent algorithm for weight optimization that tends to get trapped in local minima. A new scheme is presented in our work for initializing the weights of Convolutional Neural Networks (CNNs) by part-learning using only 5% of the training data, in a minimalistic approach. The problem at hand is the classification of handwritten numeral images. The parts that are learned, by two-way CNNs, comprise of the top-half and bottom-half of the numeral image respectively, with the second half of the image kept masked. The two set of weights initialized in this manner are respectively fine-tuned on the remaining 95% training images. The probabilistic softmax scores of the two CNNs are fused in the last stage to decide the test label. Our work validates the theory inspired from human cognition that learning in stages with increasing size and complexity of the training data improves the performance over time, rather than training on the complete dataset at one go. Experiments on the benchmark MNIST handwritten English numeral dataset yield high accuracies as compared to the state of the art.
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https://github.com/JMalhotra7/CNN-Pre-Initialization-by-Minimalistic-Part-Learning-for-Handwritten-Numeral-Recognition. Accessed 20 Dec 2019
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Susan, S., Malhotra, J. (2020). CNN Pre-initialization by Minimalistic Part-Learning for Handwritten Numeral Recognition. In: B. R., P., Thenkanidiyoor, V., Prasath, R., Vanga, O. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2019. Lecture Notes in Computer Science(), vol 11987. Springer, Cham. https://doi.org/10.1007/978-3-030-66187-8_30
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