Abstract
Shepard Interpolation Neural Networks (SINN) lay a foundation addressing the flaws of deep algorithms, inspired by statistical interpolation techniques rather than biological brains it can be mathematically proven and the neuron interactions can be intuitively described. They also possess the ability to discriminate well with limited training data during the algorithm process. To enhance SINN from just regular vectorized images, we look to utilize hand designed and natural image features to help the SINN perform better on benchmark image classification data sets. We compare these input feature vectors using the SINN framework on three benchmark image classification test sets, showing comparable results to the state-of-the-art (SOTA) for a fraction of the computational and memory requirements due to SINN shallow learning ability.
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Smith, K.E., Williams, P. (2019). A Shallow Learning - Reduced Data Approach for Image Classification. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_28
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DOI: https://doi.org/10.1007/978-3-030-18305-9_28
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