  Noise injection is a fundamental tool for data augmentation, and yet there is
no widely accepted procedure to incorporate it with learning frameworks. This
study analyzes the effects of adding or applying different noise models of
varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise
models that are distributed with different density functions are given common
magnitude levels via Structural Similarity (SSIM) metric in order to create an
appropriate ground for comparison. The basic results are conforming with the
most of the common notions in machine learning, and also introduce some novel
heuristics and recommendations on noise injection. The new approaches will
provide better understanding on optimal learning procedures for image
classification.
