  Convolutional neural networks (CNNs) have demonstrated remarkable success in
vision-related tasks. However, their susceptibility to failing when inputs
deviate from the training distribution is well-documented. Recent studies
suggest that CNNs exhibit a bias toward texture instead of object shape in
image classification tasks, and that background information may affect
predictions. This paper investigates the ability of CNNs to adapt to different
color distributions in an image while maintaining context and background. The
results of our experiments on modified MNIST and FashionMNIST data demonstrate
that changes in color can substantially affect classification accuracy. The
paper explores the effects of various regularization techniques on
generalization error across datasets and proposes a minor architectural
modification utilizing the dropout regularization in a novel way that enhances
model reliance on color-invariant intensity-based features for improved
classification accuracy. Overall, this work contributes to ongoing efforts to
understand the limitations and challenges of CNNs in image classification tasks
and offers potential solutions to enhance their performance.
