Evaluating the performance of a thresholding filter on handwritten images classification task
Abstract
In this paper we evaluate the effect of a thresholding filter on the accuracy and training times of a deep neural network. The filter increases the brightness of each pixel in the input image and then applies a threshold condition that zeroes out values exceeding a preset value. Although the filter is lossy, we demonstrate improved learning performance under some use cases.
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