Pixel Normalization from Numeric Data as Input to Neural Networks
This addresses the need for efficient text-to-image transformation in neural network applications, but it appears incremental as it builds on existing normalization methods.
The paper tackles the problem of converting textual data into images for neural network input by proposing a new pixel normalization approach, which can be accelerated via GPU implementation to achieve significant computational speedup.
Text to image transformation for input to neural networks requires intermediate steps. This paper attempts to present a new approach to pixel normalization so as to convert textual data into image, suitable as input for neural networks. This method can be further improved by its Graphics Processing Unit (GPU) implementation to provide significant speedup in computational time.